SELF-INJURY SUPPORT ONLINE: EXPLORING USE OF THE MOBILE PEER SUPPORT APPLICATION TALKLIFE A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Kaylee Payne Kruzan August 2019 © 2019 Kaylee Payne Kruzan SELF-INJURY SUPPORT ONLINE: EXPLORING USE OF THE MOBILE PEER SUPPORT APPLICATION TALKLIFE Kaylee Payne Kruzan Ph. D. Cornell University 2019 There is growing interest in how technologies can be leveraged to support mental health. Accessible through the Internet and mobile applications (apps), online communities are ubiquitous and have promise in providing individuals with peer support, information, and additional resources. However, empirical evidence for the effects of participation in online communities on mental health outcomes is limited. In this dissertation I address this gap by exploring how individuals use a mobile peer support application, TalkLife, to discuss and exchange support on self- injury. Self-injury was chosen as a case study because it is a common and concerning behavior that affects many young people. While stigma associated with the behavior can prevent people from getting help, the pervasiveness of online communities and online activity around self-injury has been noted. Informal online help-seeking is a recognized, and potentially a critical, resource for these individuals. In chapter 1, I provide an overview of relevant literature on online communities, peer support, and self-injury and discuss areas in need of further study. In chapter 2, I contribute an in-depth description of self-injury related activity on TalkLife including user characteristics, natural use patterns, and common language in posts and comments. In chapter 3, I investigate the dynamics of peer support on TalkLife. I characterize the types of support solicitations and responses on the platform and investigate the relationship between them. Then, I investigate peer responsiveness by identifying individual, message, and platform factors which predict the amount of support posts received and the amount of time it takes for community members to respond to certain types of posts. In chapter 4, I combine log data and survey data in a longitudinal examination of the relationship between TalkLife use and several self-injury outcomes including self-injury behaviors, thoughts, urges, and intentions to injure. Finally, I summarize key findings, describe implications for three key stakeholder groups (designers, clinicians, researchers), and discuss future directions in the concluding chapter. Together the findings from this dissertation make several novel contributions. First, while the methods employed here have proven to be valuable in understanding other mental health conditions, few studies have applied them to self-injury communities, or to TalkLife. Second, I contribute a detailed empirical account of the dynamics of peer support and identify factors which predict the amount of support posts generate. Lastly, this work provides initial evidence for relationship between TalkLife use and self-injury outcomes over time. Keywords: self-injury, self harm, online communities, mobile application, peer support, social support, behavior change BIOGRAPHICAL SKETCH Kaylee Payne Kruzan is a doctoral candidate in the Department of Communication at Cornell University. She completed her Master of Science in Communication at Cornell in 2018 and is expected to complete her Ph.D. in 2019. Prior to this, Kaylee earned a Master of Arts in Communication at the University of Illinois at Chicago (2015), a Bachelor of Science in Psychology and a Bachelor of Arts in Communication Studies from Grand Valley State University (2011). In her graduate studies, Kaylee’s research has focused on how media technologies (broadly conceived) can be leveraged to support mental health and well- being. With this goal, she has explored the use and efficacy of a peer support application (the focus of her dissertation) and clinical applications of virtual reality. Presently, Kaylee is most interested in the potential for technologies to attenuate barriers to treatment through providing affordable access to resources, connecting individuals with peer support services, and reducing stigma. Kaylee’s research approach is often interdisciplinary, and this has led to collaborations in several labs across campus. She has led research projects in the Social Media Lab with Dr. Natalie Bazarova, the Cornell Research Program on Self- injury and Recovery with Dr. Janis Whitlock, and the Virtual Embodiment Lab with Dr. Andrea Stevenson Won. Moving forward, Kaylee hopes to continue to make contributions to research at the intersection of technology and mental health and to more deeply explore how we can harness new systems to increase protective factors and resilience for individuals who self-manage mental health symptoms. v ACKNOWLEDGMENTS I would like to extend a heartfelt thank you to everyone who has supported me over the past few years. First, thank you to my amazing committee. To my advisors, Natalie Bazarova and Janis Whitlock, you gave me confidence in my work and capacity even when I wavered in my own convictions. Natalie, thank you for welcoming me to your lab, for your guidance on my career and for your continuous support, patience, and mentorship. Janis, you have been a grounding presence for me professionally and personally, and I will forever be grateful for your kindness and support. Thank you for trusting me to carry important projects forward and for being so generous with your time, resources, and wisdom. Thanks to Andrea Stevenson Won for guiding me through deep dives into entirely new methods and research territory. After hours of conversations, programming, building/breaking things in virtual reality I can sanely say that it was all worth it. While our data didn’t make it into the dissertation, it certainly shaped me as a scholar and solidified my commitment to exploring how technologies can be leveraged to support well-being. Jason Washburn, thank you for agreeing to be part of my doctoral committee and for your responsiveness and positivity throughout this process. I wanted my work to be useful for clinicians and individuals who self-injure, and your perspective and professional feedback was invaluable. I also want to thank early mentors at Cornell who encouraged and supported me in following my evolving research interests. Poppy McLeod, thank you for giving me such freedom when I first set foot in your lab. I learned a great deal about research vi methods from you and you’ve instilled in me a high standard of rigor for my work and others. To Lee Humphreys, thank you for showing me kindness and enthusiasm, and for asking questions which helped me think about how my research can be valuable to my participants and shaped my future direction. To my colleagues, collaborators, friends, and research assistants in the Social Media Lab, Youth Risk & Opportunity Lab, and Virtual Embodiment Lab, thank you for your support. To my cohort, and writing group, your friendship carried me through these past few years. Thanks also to Stephen Parry at the Statistical Consulting Unit – for your patience and good humor. Thank you to Jennifer Russell and Jamie Druitt, and the entire team at TalkLife. Without your support and genuine care for the TalkLife community, this dissertation would not have been possible. Thank you to my family, especially my parents and my brother Brendan, you have always been my best sounding boards and biggest supporters. Thank you also to my grandparents, who provided me with a solid and loving foundation and instilled in me the belief that I could accomplish anything I set my mind to. Finally, thank you to two beings that I cannot imagine these past few years without. Conley Wouters, thank you for seeing me through the ups and downs of life and supporting me unconditionally. And, thank you to Stevie, my sweet pup who was quite literally at my side, or on my lap, for every last keystroke. vii TABLE OF CONTENTS BIOGRAPHICAL SKETCH ........................................................................................................... v ACKNOWLEDGMENTS ............................................................................................................... vi TABLE OF CONTENTS .............................................................................................................. viii LIST OF FIGURES ......................................................................................................................... ix LIST OF TABLES ........................................................................................................................... x CHAPTER 1 INTRODUCTION .................................................................................................... 1 CHAPTER 2. DESCRIBING USERS AND USE PATTERNS ON TALKLIFE ........................... 30 CHAPTER 3. PEER SUPPORT ON TALKLIFE .......................................................................... 67 CHAPTER 4. EXAMINING THE RELATIONSHIP BETWEEN TALKLIFE USE AND SELF- INJURY OUTCOMES ................................................................................................................. 120 CHAPTER 5. GENERAL DISCUSSION AND IMPLICATIONS .............................................. 161 Appendix A ................................................................................................................................... 197 Appendix B ................................................................................................................................... 198 Appendix C ................................................................................................................................... 199 Appendix D ................................................................................................................................... 200 Appendix E ................................................................................................................................... 201 Appendix F ................................................................................................................................... 202 Appendix G .................................................................................................................................. 203 Appendix H .................................................................................................................................. 205 Appendix I .................................................................................................................................... 206 Appendix J ................................................................................................................................... 207 Appendix K .................................................................................................................................. 208 Appendix L ................................................................................................................................... 209 References .................................................................................................................................... 210 viii LIST OF FIGURES Figure 1. Distribution of posts and comments .............................................................. 81 Figure 2. Hierarchical coding scheme for posts ........................................................... 83 ix LIST OF TABLES Table 1 NSSI Functions ................................................................................................ 11 Table 2 Risks and Benefits of Online Communities for Self-Injury ............................ 20 Table 3 User Characteristics ......................................................................................... 38 Table 4 Average Use Patterns ...................................................................................... 40 Table 5 User Activity (Percentage of Users Engaging with Features) ......................... 41 Table 6 Descriptives of Posts and Comments .............................................................. 45 Table 7 Overall Model Fit ............................................................................................ 50 Table 8 Basic Descriptives for Three-Profile Model of Active Use ............................ 53 Table 9 Most Common Unigrams in Posts and Comments ......................................... 56 Table 10 Most Common Bigrams in Posts and Comments .......................................... 57 Table 11 Language Dimensions Corresponding to Top 1000 Words From Posts and Comments ..................................................................................................................... 60 Table 12 Post Coding Scheme and Percentages of Codes in Dataset .......................... 86 Table 13 Comment Coding Scheme and Percentages of Codes in Dataset .................. 89 Table 14 Model Predicting Comment Volume .......................................................... 104 Table 15 Model Predicting Reaction Volume ............................................................ 106 Table 16 Model Predicting Response Time ............................................................... 107 Table 17 Direction of Relationships for All Predictors and Response Variables ...... 110 Table 18 Participant Characteristics ........................................................................... 142 Table 19 Predictors of Self-Injury Outcomes ............................................................. 145 Table 20 Predictors of Self-Injury Frequency ............................................................ 148 Table 21 Significant Predictors by Outcome .............................................................. 186 x CHAPTER 1 INTRODUCTION The National Comorbidity Study estimates that 26% of the U.S. population met criteria for a psychiatric disorder within the past 12 months (Kessler, Chiu, Demler, & Walters, 2005) and 46% of the population will meet criteria over the course of life (Kessler, Berglund et al., 2005). While symptoms associated with mental illness can vary from severe and debilitating, to subclinical or having minor influence over functioning, the proportion of individuals who receive care falls far under documented need (Kessler, Demler, et al., 2005). This treatment gap has catalyzed efforts among researchers and clinicians to identify factors contributing to the burden of mental illness (Teachman, McKay, Barch, Prinstein, Hollon & Chambless, 2018) and to determine how to broadly disseminate psychosocial interventions (Kazdin, 2017). Online venues, such as online support communities, have been widely acknowledged as an alternative, or conduit, to more traditional treatment modalities (Kazdin & Blasé, 2011; Kazdin, 2017). Online Communities for Mental Health The majority of the U.S. population goes online at least once daily (77%) (Pew Internet Research, 2018) and over ¼ of U.S. Internet users have sought information or support from online communities (Griffiths et al., 2009). The attraction to online spaces for help-seeking among individuals with stigmatized mental or physical conditions seems to be in part because the Internet can transcend practical barriers associated with formal help (e.g., accessibility, cost, and convenience; Kazdin, & 1 Blasé, 2011; Kazdin, 2017; Adkins et al., 2017) and can attenuate psychological barriers associated with disclosing struggles (Walther & Boyd, 2002). Efforts to understand how, and under what conditions, online communities can assist individuals in need of support are of imminent importance. What is an Online Community? A variety of different online platforms exist for individuals in need of help or support around mental illness. These online communities—sometimes referred to as online peer support communities, online support groups, or peer-to-peer social networks—have evolved greatly over the past several decades. For example, a published repository of online sources for emotional support in the late 1990s included discussion groups, email lists, and Usenet groups which were mostly asynchronous and hierarchical in nature (e.g., email lists with moderators; Harris, 1996 cf. Walther & Boyd, 2002). These traditional online communities united individuals with shared concerns but were constrained by mostly textual forms of communication and similar in site structure. Today the landscape of online communities has expanded immensely. These platforms are often heterogeneous, are more flexible in terms of how they can be used (e.g., online, mobile), and have richer media affordances (e.g., multiple, synchronous and asynchronous channels of communication, selective anonymity). Online communities include message boards, websites, or applications which are independent of a member’s primary social network (e.g., illness-specific forums), as well as subgroups that are part of mainstream social media sites (e.g., Facebook’s Depression group, Lerman et al, 2017; Reddit’s r/depression thread, De Choudhury et al., 2014). In the prior case, some new, emerging, platforms blend the illness or 2 symptom-specific focus of traditional online forums with the rich features of popular social media (e.g., TalkLife, TalkSpace, 7Cups). In this dissertation, the term “online community” is used liberally to refer to any platform that provides users with (1) a space to share and solicit information from other members and (2) a way to show support or provide direct feedback to others. Usually these sites allow members to engage actively, by posting or commenting, or passively, through lurking or consuming others’ content. While the ways in which individuals engage through these communities can vary significantly, past work has shown that computer-mediated communication can prompt intimate disclosures which may have therapeutic valuable (Coulson, Bullock, & Rodham, 2017). Affordances of Computer-Mediated Communication A sizable body of literature shows that the affordances of computer-mediated communication (CMC) can reduce barriers associated with help-seeking (Walther & Boyd, 2002). One of the most noted affordances of CMC is anonymity. Individuals can more easily choose when, and what, personal details they reveal online. This anonymity contributes to a disinhibition effect, which, in turn, reduces concerns about being identified by others (De Choudhury & De, 2014; Valkenburg & Peter, 2009; Walther, 1996; Jiang, Bazarova, & Hancock, 2011, 2013; Bazarova & Choi, 2014) and increases comfort beyond that of face-to-face environments when sharing emotions or problems with peers (Suler, 2004; De Choudhury & De, 2014; Andalibi, Öztürk, & Forte, 2017). Indeed, research has shown that individuals engage in sensitive self- disclosures around mental, or physical, health in online environments (Smithson, Sharkey, Hewis, Jones, Emmens, Ford & Owens, 2011; Seko & Lewis, 2016). 3 Moreover, despite concerns about disinhibition leading to negative online behaviors (e.g., trolling, cyberbullying; Suler, 2004), research has shown that sensitive disclosures are frequently met with supportive comments (Andalibi et al., 2017) or reciprocal disclosure (Laurenceau, Barret, & Pietromonaco, 1998). Social distance is another key benefit of online interactions. In online communities, individuals can connect to weak ties, or people who are not directly in their social circle (Granovetter, 1977; Walther & Boyd, 2002). While strong ties, such as family and close friends, can provide support and resources to individuals with mental illness, those suffering from stigmatized illnesses can be reticent to disclose struggles for fear of being misunderstood or rejected. Some of these concerns around disclosure are mitigated when connecting with weak ties for support (Wright & Rains, 2013). Indeed, research has shown that weak-tie support carries less social risk, is typically more objective, and is preferred when individuals feel their condition is stigmatized (Wright & Rains, 2013; Wright, Rains, & Banas, 2010). Online communities are typically comprised of weak-ties who can provide peer support, a specialized type of social support which involves the exchange of experiential knowledge (Thoits, 2011; Proudfoot et al., 2012). Connecting to peers who have shared mental health experience can be beneficial because it promotes the exchange of expertise and can attenuate feelings of isolation (Walther & Boyd, 2002). The asynchronous nature of interactions online also affords greater control over message construction and self-presentation. Relaxed constraints around responding to posts may enable individuals to more deliberately or thoughtfully communicate about highly personal or distressing topics. In a study comparing 4 emotional expression in online and offline cancer support groups, for example, researchers found higher emotional expression and more frequent advice exchanged online (Setoyama, Yamasaki & Nakayama, 2011). Relatedly, individuals can observe each other’s interactions and gain important information without having to disclose anything themselves (i.e., lurking). Research has shown that this type of online engagement, lurking, is common (Whitlock, Powers, Eckenrode, 2006; van Mierlo, 2014; Nonnecke & Preece, 2000) and it may be less intimidating for individuals when they first seek help (Walther & Boyd, 2002). While the affordances of CMC seem conducive to sharing and obtaining support around mental illness, the vast majority of research on online support groups for mental health relies on self-report and links participation to psychological effects theoretically, not empirically (with some notable exceptions, e.g., Lawlor et al., 2004). For example, participation in online peer support groups has been associated with self- reported empowerment, decreased social isolation, and lessening of mental health symptoms in several studies (Barak, Boniel-Nissim, & Suler, 2008; Barak & Dolev- Cohen, 2006; Naslund et al., 2016; van Uden-Kraan et al, 2009; D’Agostino et al., 2017; Johnson, Zastawny, & Kulpa, 2010). These effects are theorized to be driven by the exchange of informational, emotional, and companionship support (Naslund et al. 2014). Additionally, the public nature of comments may lead to a heightened sense of accountability to oneself and the larger community and may augment effects (Aschbrenner, Naslund, & Bartles, 2016). Researchers also propose that online interactions can serve as a catalyst for offline disclosures (McKenna & Bargh, 1998) or more formal mental healthcare (Powell, McCarthy, & Eysenbach, 2003). In line 5 with this, participation in online communities may represent a critical point in an individual’s effort to cope with, or improve, their mental health and well-being. The proliferation of online communities for mental health, coupled with the growing mental health burden, suggests the critical role these spaces may play in supporting individuals with mental health and demands further research (Kazdin, 2017). In particular, there is interest in how these communities can be supportive for individuals who are not otherwise likely to engage in treatment. A recent survey found that individuals who score high on measures of suicidal ideation prefer online over either offline help-seeking or a no treatment at all (Wilks, Coyle, Krek, et al., 2018). Also, individuals without past engagement in treatment typically endorsed more barriers to offline treatment and preference for online help (Wilks, et al., 2018) Significance of NSSI Non-suicidal self-injury is a behavior that individuals are often hesitant to disclose or seek treatment for (Michelmore & Hindley, 2012; Fortune, Sinclair, & Hawton, 2008). While there is not consensus on whether non-suicidal self-injury constitutes a mental disorder in, and of, itself, it is a widespread and concerning behavior that can result in lethal outcomes (Hasking & Boyes, 2018; Andover, Morris, Wren, & Bruzzese, 2012). Consequently, there is growing concern around how to identify and help these individuals. Online activity related to self-injury (e.g., self- injury related searches and participation in online communities) can provide key insights to this end. In what follows, I describe the significance of non-suicidal self- injury, review research on self-injury and online communities, and then I describe a series of studies aimed at rigorously examining how an online peer support 6 community impacts mental health for individuals who self-injure. What is Non-Suicidal Self-Injury? Non-suicidal self-injury (NSSI) is defined as the “deliberate, direct destruction of body tissue without suicidal intent and for purposes not socially sanctioned” (International Society for the Study of Self-Injury, 2018). This includes behaviors such as cutting, burning, and banging/hitting one’s body. While NSSI can be associated with psychological morbidity (e.g., depression, anxiety, eating disorders, borderline personality disorders), it can occur independent of other diagnoses (Bentley et al., 2014; Klonsky, 2007). It is estimated that 13% of American adolescents have self- injured at some point in their lives (Swannell, Martin, Page, Hasking, & St. John, 2014) and estimates are higher in college populations (Gratz, 2001; Jacobson & Gould, 2007). The typical age of onset for NSSI is between adolescence and young adulthood (between 14 and 24 years old; Favazza & Conterio, 1989; Whitlock et al., 2006), however first instances of self-injury have been documented at nontrivial rates in children. In a community sample, 8% of primary school age children (7 – 10 years of age) reported self-injury (Barrocas, Hanin, Young, & Abela, 2012). By definition, NSSI behaviors are enacted without suicidal intent; however, the outcomes of NSSI can be serious (Andover, Morris, Wren, & Bruzzese, 2012; Muehlenkamp & Guiterrez, 2004; Nock et al., 2006). Among college students with NSSI, nearly 1/5 reported damaging their body more than intended, while only 5% of those with NSSI sought medical assistance for their wounds (Whitlock et al., 2011). Moreover, while NSSI behaviors can be distinct from suicidality, the two often co- occur (Andover et al., 2012; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 7 2006; Victor, Styer, & Washburn, 2015) and NSSI is a potent risk factor for future suicide ideation and acts (Klonksy, May & Glenn, 2013; Whitlock et al., 2013). For example, some work finds that adults with a history of NSSI are twice as likely as participants with other mood disorders to engage in suicide attempts (Chesin, Galfavy, Sonmez, Wong, Oquendo, Mann, & Stanley, 2017). Age of NSSI onset is also predictive of future suicide risk, and earlier onset of NSSI is associated with greater risk (Chesin et al., 2017). Thus, early intervention efforts are of critical, and imminent, importance. Who Does It Affect? Several factors which may increase vulnerability to NSSI including: childhood sexual abuse (Maniglio, 2011; Liu, Scopelliti, Pittman, & Zamora, 2018), high emotional reactivity and lower tolerance of emotions (Hasking et al., 2016), internalizing symptoms associated with depression, anxiety, hopelessness (Fox et al., 2015) and other biologic or genetic factors (Maciejewski et al., 2014). Given that the etiology of NSSI can have social, emotional, and/or biological origins, it is not surprising that epidemiological data documents great heterogeneity in NSSI populations (Whitlock et al., 2008). Indeed, prevalence rates do not appear to differ greatly by geographical region. NSSI has been documented at similar rates in over 18 countries with individuals of different racial and socioeconomic backgrounds (Swannell, Martin, Page, Hasking, & St. John, 2014; Whitlock et al., 2006; Marshall & Yazdani, 1999). While some studies have shown that self-injury is more common among women (Zlotnick et al., 1999), more recent studies suggest that men engage in these behaviors at comparable rates 8 (Gratz, 2001; Bebbington et al., 2010). However, there do appear to be some differences in the clinical presentation of NSSI by gender (Victor et al., 2018) and the methods endorsed. For example, males being more likely to engage in self-battery and less likely to use NSSI for internal functions and women are more likely to engage in cutting or scratching for affect regulation purposes (Muehlenkamp, Yates, & Alberts, 2004; Whitlock, Eckenrode, & Silverman, 2006). Many individuals also report engaging in more than one method to serve a number of functions (Gratz, 2001). Some research suggests that individuals who identify as other than heterosexual are at greater risk for NSSI (Batejan, Jarvi, & Swenson, 2014; Whitlock et al., 2011). In one study, individuals who identified as bisexual, or gay or lesbian, were nearly 4 and 2 times, more likely to report NSSI behaviors relative to individuals identify as heterosexual, respectively (Whitlock et al., 2011). These findings converge with previous work on higher mental health risk (depression, anxiety, substance use) among sexual minorities (Fergusson, Horwood, & Beatrais, 1999; King et al., 2008). What Are the Functions? Functional accounts of NSSI assume that the behavior has proximal determinants (Nock, 2009) which largely fall into two categories: (1) intrapersonal functions, or efforts to manage or change one’s internal state and (2) interpersonal functions, aimed at influencing one’s external environment (Klonsky et al., 2015; Klonsky, 2007; Klonsky & Glenn, 2009; Nock & Prinstein, 2004) (See table 1 for more detail on these two functions). A recent meta-analysis reports that intrapersonal functions are more common (66 - 81%), than interpersonal functions (32 – 56%), and that emotion regulation is the single most prevalent function cited (63-78%) (Taylor et 9 al., 2018). Indeed, the most robust empirical evidence is for the role of NSSI in attenuating negative affect. Intrapersonal functions. Some research describes NSSI as a mechanism for emotion regulation (Hasking et al., 2016; Klonsky, 2007). Emotion regulation can involve both generating and diminishing certain affective states. In laboratory studies self-injury proxies (e.g., exposure to self-injury imagery, the cold pressor task) have been shown to diminish negative affect and arousal (Klonsky, 2007; Russ et al., 1992; Haines et al. 1995). NSSI has also been documented to increase positive affect (Whitlock, Exner-Cortens, & Purington, 2014) and may be enacted to break free from states of dissociation (Klonsky, 2007; Klonsky, Glenn, Styer, Olino & Washburn, 2015; Snir et al., 2015). Studies employing ecological momentary assessments have confirmed the role of self-injury in regulating emotions. In one study, participants were asked to record their affect around episodes of NSSI for 7 consecutive days. Findings revealed increases in negative affect prior to NSSI acts, which subsequently faded away gradually with enactment of self-injury behaviors (Armey, Crowther, Miller, 2011). Muehlenkamp et al. (2009) similarly found increases in negative affect and decreases in positive affect prior to an NSSI event. NSSI has also been associated with a reduced capacity to accept and differentiate emotions (Zaki, Coifman, Rafaeli, Berenson, & Downey, 2013; Gratz & Roemer, 2004). Specifically, Gratz and Roemer (2004) describe that an inability to accept emotions can lead suppression or efforts to control emotions through maladaptive behaviors like NSSI. Indeed, past work has shown that individuals who 10 engage in NSSI have difficulties accepting both negative and positive emotions (Allen & Hooley, 2015; Hasking et al., 2016). By contrast, emotional differentiation – being able to identify nuanced emotional states—appears to be a protective factor for individuals who self-injure (Zaki et al., 2013). In an experience sampling study, Zaki et al. (2013) found that participants with lower abilities to differentiate emotions and higher dispositional rumination reported more NSSI episodes. In sum, deficits in emotional differentiation and acceptance, coupled with the noted emotion regulatory function of NSSI, make behavioral cessation challenging. Table 1 NSSI Functions Intrapersonal Interpersonal Emotion regulation Influence others’ behaviors Anti-dissociation Communicate distress Self-punishment Punish others Sensation seeking Peer bonding Interpersonal functions. Interpersonal functions of NSSI have also been noted. For example, Snir, Rafaeli, Gadassi, Berenson, and Downey (2015) conducted a 3-week long diary study assessing explicit and inferred motives around self-injury. Participants were prompted to record affective, interpersonal, and behavioral experiences up to 5 times a day. Findings revealed that feelings of isolation or rejection coincided with NSSI urges hours prior to an actual NSSI act (Snir et al., 2015). Moreover, interpersonal distress appeared to diminish after self-injury urges even when an individual did not engage in an act of self-injury. Using a similar method, Turner, Cobb, Gratz, and Chapman (2016) had individuals with NSSI complete diary entries regarding their urges, mood, conflict and 11 perceived support at three time points throughout the day for 2 weeks. Findings showed that interpersonal conflict was related to greater same-day NSSI urges and acts. Moreover, the act of revealing NSSI to others was associated with greater perceived social support; however, this perceived support also increased the likelihood of an individual engaging in NSSI the following day. In other words, individuals who disclosed their NSSI to others during the study reported more perceived support and more total NSSI acts. While these findings may seem paradoxical, they provide support for the interpersonal reinforcement function of NSSI behaviors—that is, NSSI may be reinforced by desired changes in the environment or a reduction of aversive influences (Nock & Prinstein, 2004; Klonsky, 2007). Together, empirical studies of NSSI strongly support its dual interpersonal and intrapersonal functions. Although our understanding of NSSI has grown immensely over the last decade, effective treatment and intervention strategies remain a challenge. In the following section, I will briefly summarize what is known about NSSI behavior change and common barriers to help-seeking and treatment. Then, I will discuss research that demonstrates the potential for the Internet to attenuate some of these barriers and facilitate the recovery process. Treatment for Non-Suicidal Self-Injury A review of NSSI treatment is beyond the scope of this paper (for comprehensive reviews in adult populations see Hawton et al., 2016, in adolescent population see Turner, Austin & Chapman, 2014 and in youth populations see Washburn et al. 2012 and Glenn, Franklin, and Nock, 2015, respectively). For the present purposes, I will briefly describe one of the common treatments for NSSI in 12 order to highlight empirically-supported mechanisms of change. Then I will present prior research on online communities for self-injury and discuss how these communities may facilitate some of these key mechanisms. Dialectical behavior therapy (DBT) was originally developed for the treatment of borderline personality disorder (BPD) (Linehan, 1993); however, the treatment has also shown promise for self-injury, one of BPD’s prominent symptoms (Muehlenkamp, 2006). This structured therapy approach involves individual and group-based sessions which aim to uncover intra- and interpersonal factors that maintain NSSI related events (Linehan, 1993; Muehlenkamp, 2006). Specifically, the treatment has four modules which target mechanisms of change including: distress tolerance, emotion regulation, interpersonal effectiveness, and mindfulness (Linehan, 1993). The reduced capacity to experience, and respond to, emotions commonly associated with NSSI (Hasking et al., 2016), is well addressed in DBT’s modules, as well as other therapeutic approaches such as acceptance and commitment therapy (ACT) (Klonsky, 2011). In DBT there is a focus on addressing skill deficits and replacing maladaptive practices with new coping skills such as cognitive restructuring and emotion regulation strategies including mindfulness and problem-solving (Miller, Wyman, Huppert, Glassman & Rathus, 2000; Shearin & Linehan, 1992; Linehan, 1993). In addition to building intrapersonal coping strategies, DBT has an emphasis on interpersonal skill building through group and family sessions which focus on identifying relational patterns and practicing effective communication (Glenn, 13 Franklin, & Nock, 2015; Muehlenkamp, 2006; Fleischhaker et al., 2011). Establishing new communicative patterns, and learning new healthy ways to solicit support, can be especially useful for those who report interpersonal functions of NSSI. A final component which has been theorized to be useful for individuals who self-injure is to challenge common feelings of shame and guilt through self- compassion (Van Vliet & Kalnins, 2011; Hooley, Ho, Slater, & Lockshin, 2010; Xavier, Pinto-Gouveia, & Gunha, 2016). While DBT does not directly address self- compassion, it is related to some of the mindfulness skills. Clinical trials indicate the DBT can reduce self-injurious acts in adult populations, and there is evidence, albeit limited, for its efficacy in adolescent populations (Fleischhaker et al., 2011; Washburn et al., 2012; Cook & Gorraiz, 2016). Because age of onset can be quite young, the need for work examining treatment efficacy among youth and adolescent populations is critical (Washburn et al., 2012; Glenn et al., 2015). Additionally, research suggests that effective intervention at a young age may prevent future NSSI and possibly suicide. Online support communities and online intervention represent one method for reaching these young individuals. Barriers to Help-Seeking and Treatment A critical challenge of engaging individuals who self-injure is low disclosure rates (Evans, Hawton, & Rodham, 2005, Nixon, Cloutier, & Jansson, 2008; Whitlock et al., 2011). Among college-aged individuals with a history of NSSI, just under 40% reported that no one knew of their self-injury (Whitlock et al., 2006). In another sample college-aged sample, about 10% of individuals reporting having disclosed their self-injury to mental health professionals (Whitlock et al., 2011). Low disclosure is 14 exacerbated by the fact that many individuals who engage in NSSI maintain regular school or work attendance and may function on par with their peers (Whitlock et al., 2006). This means that it is possible to keep injuries concealed from even their closest friends and family and, as a result, they may not gain access to resources that would promote the aforementioned skills (e.g. distress tolerance, emotion regulation, interpersonal effectiveness, self-compassion) for recovery. Some individuals do not perceive NSSI as a problem (Fortune et al., 2008). Of individuals who reported current self-injury behaviors in one study, only 20% felt that it interfered with their daily life (Whitlock, Exner-Cortens, & Purington, 2014). Indeed, even those classified as having the most severe symptoms sometimes fail to see the behaviors as problematic (Whitlock, Muehlenkamp, & Eckenrode, 2008). Self- injury is often seen as a fast and highly effective strategy for coping (Lewis & Baker, 2011), which can make individuals resistant to clinical and non-clinical intervention (Zila, & Kiselica, 2001). Therapeutic protocols, including DBT, usually involve an introduction to alternative coping strategies (Linehan,1993; Muehlenkamp, 2006) but the development of new coping skills can take time and when self-injury is already seen as a quick method for coping, this belief can impede upon skill development and the general change process. Lastly, NSSI is a frequently misunderstood and stigmatized behavior and this contributes to non-disclosure, on account of shame or embarrassment (Rowe, French, Henderson et al., 2014; McHale & Felton, 2010). Reactions to self-injury often involve disgust or apprehension and, stigma towards NSSI behaviors is frequently referenced as a primary impediment to help-seeking (Rowe et al., 2014; Fortune et al., 15 2008). Not knowing where to get help, who to trust, and fear that their behaviors will be misinterpreted as attention seeking are also among frequently cited deterrents to help-seeking (Hawton et al., 2012, Fortune, Sinclair, Hawton, 2008; Michelmore & Hindley, 2012). To summarize, at least three overlapping issues serve as barriers to NSSI help- seeking and treatment: (1) reluctance to disclose, (2) the perceived efficacy of self- injury and resistance to intervention, and (3) stigma. Future interventions must consider the extent to which they can attenuate these barriers and provide individuals with support tools to help them manage mental health. As noted above, the Internet has become an increasingly accessible, and frequently utilized, platform for information and support seeking. Self-Injury Online Researchers have considered the utility of online communities for individuals who self-injure for over a decade. In general, this work has (1) established that online information and help-seeking among individuals who self-injure is pervasive, (2) uncovered motives for engaging in online spaces, and (3) examined several risks and benefits of using these platforms. Each will be discussed below before moving onto a discussion of what research is still needed. Prevalence of Self-Injury Online Activity Self-injury information-seeking occurs at high rates online. In 2006, for example, more than 400 self-injury message boards were identified (Whitlock, Powers, & Eckenrode, 2006) and in 2014 Google search terms related to NSSI were sought out more than 42 million times (Lewis et al., 2014). Additionally, some 16 research suggests that individuals who self-injure may use the internet more often than their peers (Mitchell &Ybarra, 2007; Frost & Casey, 2016; Lewis & Michal, 2016). In one study, nearly one-third of young people with a history of self-injury had reported online help seeking (Frost & Casey, 2016). Further, these individuals were significantly more distressed and suicidal than those who had not sought help online (Frost & Casey, 2016). Motives Individuals report seeking self-injury related information on the internet for a variety of reasons. Among these motives, seeking validation for experiences, information about self-injury, and peer support are prominent (Lewis & Michal, 2016; Lewis, Rosenrot, & Messner, 2012; Rodham, Gavin, & Miles, 2007; Whitlock et al., 2006). Research suggests that motivations to engage in online communities may evolve over time. For example, in a study of posts in a self-injury community, individuals at early stages in NSSI recovery were over-represented on these sites, perhaps suggesting that contact with the online community is a first step towards NSSI behavior change for some (Grunberg & Lewis, 2015). Other research shows that over time exposure to triggering content, stress, or feeling that their needs were unmet can lead to breaks in participation (Lewis & Michal, 2016). However, individuals also report going back to online communities as a form of crisis management, particularly when they felt vulnerable to relapse (Coulson, Bullock, & Rodham, 2017). Some work suggests that online communities can serve as a distraction from urges and a coping mechanism in times of crisis (Baker & Fortune, 2008; Rodham et al., 2007). Content 17 Online communities for self-injury differ in their primary affordances. For example, some research has looked at YouTube, which is a video-based medium (Lewis & Knoll, 2015), whereas other work has focused on text-based communities (e.g. Rodham, Gavin, & Miles, 2007) or communities on sites where images are the primary format of expression (e.g. Instagram, Brown et al., 2018). However, content analyses of these sites find an exchange of similar topics including peer support, venting, triggers, concealment methods, and formal help seeking (Whitlock et al., 2006). Individuals exchange practical information in regard to handling wounds (Lewis & Knoll, 2015), personal stories, and recovery messages (Seko & Lewis, 2016). While content can be neutral in regard to whether individuals should seek NSSI treatment (Lewis & Knoll, 2015; Lewis & Baker, 2011), there is evidence for an exchange of graphic content discussing methods and plans (Marchant et al., 2017; Baker & Lewis, 2013). Feedback Some research has examined how content is received by the community to gain insights on community norms and to assess risks and benefits. For example, Brown et al. (2018) looked at commenting patterns on wound-related posts on Instagram. Researchers found that post with wounds received twice as many comments as posts without wounds and that the severity of wounds was positively associated with the number of overall comments. In an analysis of Tumblr, Seko and Lewis (2016) found an equal proportion of hopeless and hopeful posts related to NSSI. However, posts depicting wounds were shared far less than content that conveyed indirect self-injury or general hopelessness. The contrast between engagement of 18 wound-related content on these two sites suggests different motivations for commenting on, and sharing, others’ content. In the first case, comments (which were mostly empathic) may represent a direct way to provide a community member with feedback or support regarding their distress; however, sharing this type of content increases its reach and potential to trigger others. Benefits Many of the benefits of engaging in online communities for self-injury mirror those in the previously reviewed literature on online support. Indeed, the greatest benefit of online support around self-injury appears to be this sense of community and exchange of experiential knowledge (Marchant et al., 2017). For individuals who self- injure, the stigmatization of the behavior can prevent them from receiving this type of social support in more traditional offline environments. Relationships in these spaces can decrease feelings of isolation, and lead to sense of purpose, and feelings of acceptance or being understood (Daine et al., 2013). Moreover, evidence shows that individuals exchange emotional and informational support (including coping strategies) without the fear of judgment (Duggan et al., 2012; Lewis, Heath, St. Denis, & Noble, 2011; Lewis & Michal, 2016). In one study, greater engagement in an online community was associated with reduced self-injurious behavior via self-report (Murray & Fox, 2006). A recent survey assessing the therapeutic affordances of a self-harm online support community found evidence for the benefits of connection (e.g. mutual support) and exploration (e.g. learn strategies, seek information), that it allowed them to narrate their experiences and self-reflect (e.g. share experiences, personal clarity), 19 and that they liked the autonomy and anonymity they had in choosing how to present themselves (Coulson, Bullock, Rodham, 2017). Individuals also frequently report accessing online communities in order to better understand their NSSI behaviors (Lewis & Michal, 2016). Several mechanisms including social comparison and social learning, through the sharing of one’s story and bearing witness to others, may bring individuals into closer contact with self-insight and provide them with a space to reflect on the root cause of their behavior. Moreover, reflection on these sites may reveal information about situations, or patterns, which are particularly triggering for the individual. In addition to receiving support from others, individuals can, and do, frequently transition to providing support for other members (Whitlock et al., 2006; Smithson et al., 2011). A great deal of literature indicates that the provision of peer support can have a unique and powerful effect on the provider (Riessman, 1965; Moran et al., 2012). The act of telling one’s story in the context of providing peer support has been associated with increased empowerment, self-esteem, interpersonal competence, and an improved sense of self through social approval (Skovholt, 1974, p. 62). Furthermore, this act of transitioning from solicitation to provision may mark meaningful progress towards recovery and increase an individual’s commitment to recover (Whitlock et al., 2006; Smithson et al., 2011; Riessman, 1965). Table 2 Risks and Benefits of Online Communities for Self-Injury Risks Benefits Normalization and reinforcement Support Diminish motivation for formal help- Increased validation and acceptance seeking Focus on emotional suffering can Belongingness 20 lead to rumination Viable coping strategy Resources—coping tips Content may trigger Recovery focused messages Risks While many people reap benefit from participating in online peer-to-peer support networks, there are notable risks. First, while the availability of information on NSSI online marks a positive step in mental health literacy, Lewis, Mahdy, and Michal (2014) found that popular search engines (e.g. Google) often return health- information sites that “yield non-credible and low-quality information that may propagate common NSSI myths” (p. 447). This biased information can be detrimental to those seeking help for themselves or for their loved ones. Other popular sites, like YouTube, have been adopted to disseminate practical information on self-injury like how to care for wounds (Lewis and Knoll, 2014). Again, while this information is important from the perspective of immediate health and safety, some scholars caution that the self-help structure of the internet may dissuade people from more formalized treatments, medical attention, or contribute to a normalization of these behaviors (Lewis & Knoll, 2014). Another noted risk associated with online communication stems from the same mechanism that affords great benefit: online disinhibition. Research has shown that online disinhibition can lead to rude or harsh comments, however, on social networks of people with a common concern or intention towards recovery, lowered inhibition typically leads to more social support rather than criticism (De Choudhury & De, 2014). One of the most documented risks associated with online communities for 21 individuals who self-injure is that they can lead to the normalization of self-injury behavior (Rodham, Gavin, Miles, 2007; Whitlock et al., 2006, Smithson et al., 2011). Even with site moderation, individuals can be exposed to triggering graphic or emotional images or text (Lewis & Baker, 2011; Baker & Lewis, 2013; Lewis & Michal, 2016), including tips on concealment, suicidal ideation, or plans (Dyson, Hartling, Shulhan, Chisholm, Milne, Sundar et al., 2016). Moreover, some studies indicate that a subgroup of individuals access online communities in order to sustain or trigger self-injury and share maladaptive techniques (Murray & Fox, 2006; Lewis & Seko, 2016; Whitlock et al., 2006). Increased exposure to self-injury related content has been associated with decreased aversion to self-injury and this can lead to future suicidal ideation (Franklin et al., 2016). Another noted risk stems from the investment in, and the sense of belonging individuals have on, these sites. While a sense of belonging is a key component of recovery, sites that are structured around a shared maladaptive practice, may keep individuals locked into a self-injury (SI) identity. One may feel the need to maintain maladaptive behaviors in order to remain part of the group, particularly if they do not have other support systems offline (Whitlock et al., 2006). Similarly, an overreliance on online communities may take away from changes that need to happen in individuals’ offline lives (e.g. confronting people, developing alternative coping mechanism) (Whitlock et al., 2007), or lead to further isolation offline (Baker & Fortune, 2008). For instance, while emotional support is influential in recovery from mental illness, this support may detract from the severity of the behavior, potentially slowing the change process (Dyson et al., 2016). 22 A final risk associated with online communities, and media exposure more generally, is potential social contagion of emotion or behavior. Empirical work suggests that self-injury, like suicide, can result from exposure to peers who enact said behaviors or intentions (Jarvi, Jackson, Swenson, & Crawford, 2013). Emotional contagion has been documented in online communities for other issues due to an over- sharing of negative emotion among individuals with preexisting depressive tendencies (Takahashi et al. 2009; Easton et al., 2017; Whitlock et al., 2007). In an online community for depressed individuals, for example, some members experienced depressive spirals as a result of exposure to negative information or over-involvement (Takahashi et al., 2009). Contagion has been documented in non-clinical, school, and community samples (Nock, Prinstein, & Sterba, 2009; Prinstein, Heilbron, Guerry, Franklin, Rancourt, Simon, Spirito, 2010). Some work suggests that the presence of self-injury in the media has grown and may be a way that vulnerable individuals learn about NSSI, or consider it as a coping strategy (Whitlock, Purington, & Gershkovich, 2009). Indeed, exposure to self-injury-related material has been associated with increased prevalence (Muehlenkamp et al., 2008). However, most evidence suggests that social contagion may play a role in first instances of NSSI—and far less work has explored social contagion from a dose-response perspective in online forums. While the risks of online communities are concerning, there is reason to believe that adverse effects result in a minority of cases (Griffiths et al., 2009). For example, among those who responded to a survey on online forum use, less than 9% reported any negative effects (Easton et al., 2017). Similarly, 11% of respondents from 23 online message board for self-injury reported that the board triggered self-harm, the majority (73%) said that overall the site reduced their self-injury behaviors (Murray & Fox, 2006). Even with low rates, it is important to be mindful of the risks and researchers need to better understand under what conditions participation in online communities may lead to aversive outcomes. In sum, the evidence thus far discussed points to both positive and negative effects of participation in online communities for individuals who self-injure (Dyson et al., 2016; Seko & Lewis, 2016; Lewis & Arbhuthnott, 2014; Lewis & Seko, 2016). However, research in this area remains nascent and even the most recent systematic review identified only 26 articles on studies of social media platforms (broadly defined as peer-to-peer support sites including forums) used by young people to discuss and view deliberate self-harm (Dyson et al., 2016). Further most of this prior work is methodologically homogenous. Recent advances in computational methods have yet to be thoroughly applied to the study of self-injury behavior online. The proliferation of online communities wherein people discuss and disclose self-injury marks a need, and opportunity, for future research to explore their potential in facilitating connection, access to information, and supporting the recovery process. Problem Statement Non-suicidal self-injury is a serious public health concern. Treatments are available but access and formal help-seeking remain critical issues. Online communities have promise for connecting individuals who self-injure and can provide access to information and resources useful in attenuating the burden of self-injury. While much has been gained through the aforementioned work, there are a number of 24 pending questions which can be addressed in future research. First, descriptive studies have shed light on the types of information exchanged online in regard to self-injury, yet we have not fully examined the interpersonal dynamics of these exchanges. Past work has shown the importance of both the expressive act of initial posts as well as the impact of feedback. To make informed decisions about intervention, and to increase our knowledge of the role online communities play in the lives of individuals who self-injure, we must first understand interaction patterns. Some relevant questions are: (a) What is the relationship between solicitation and response in online communities for self-injury? (b) Are there normative behavior patterns on these social networks (both at the individual and network level)? and (c) What types of initiating content generate more or better feedback? Second, while content analysis has been useful for detecting themes and inferring motives, another methodological approach which has proven to be fruitful in understanding online communities for other health issues, is to consider behavioral trends on these platforms computationally (De Choudhury & De, 2014; De Choudhury, Gamon, Counts, & Horvitz, 2013). With this methodology in mind, some important questions include: (a) What behaviors and characteristics are common among individuals who frequent sites for self-injury? (b) How often do individuals use these sites? (c) What percentage of users are lurking or actively engaging? Further, linking behavioral and linguistic patterns will enable us to make stronger inferences about the effect of platform use on general well-being and self-injury related behavior (e.g. changes in linguistic patterns over time). 25 Lastly, at the time of writing this chapter only one study had examined exposure to self-injury content on social media experimentally. Lewis, Seko, and Joshi (2018) showed that exposure to online messages about self-injury can impact attitudes toward recovery such that viewing hopeful comments increased positive attitudes toward recovery and recovery-oriented subjective norms. There is a similar dearth in studies investigating the direct relationship between activity in online communities and subsequent NSSI behavior or offline help-seeking. Relevant questions include: (a) How does exposure to self-injury content in online communities impact behavior? and (b) What specific engagement patterns are predictive of future NSSI thoughts or behaviors? Aims When individuals use online communities for self-injury this may mark a critical point in their behavior change or recovery process. The foregoing review shows the importance of understanding (1) how individuals approach help-seeking in online formats, (2) what types of help they solicit and receive, and (3) how these online exchanges impact offline behavior (related to mental health and help seeking). The goal of this dissertation is to begin answering these questions through a thorough analysis of TalkLife, a well-populated, and freely accessible, peer-to-peer application used by individuals who self-injure. Approach (overview of chapters) To conduct a thorough investigation of an online community, researchers must first understand and describe users and user patterns. Chapter 2 is designed to meet 26 this end. In chapter 2, I present basic descriptives to answer the following questions: (1) who is using TalkLife to discuss self-injury?, (2) how do individuals use the platform?, and (3) what language attributes and behaviors characterize use among individuals who have disclosed self-injury? In summary, I find that the demographics of users on TalkLife resemble those in past work—users are mostly female and are of adolescent and young adult age. How users engage on the application varies significantly. Specifically, three archetypes of TalkLife users exist based on engagement level—low, moderate, and highly engaged users. Finally, I report common language in posts and comments based on an analysis of n-grams and linguistic categories. Chapter 3 was designed to investigate the exchange of peer support on TalkLife. In this chapter, I identify gaps in prior literature on online peer support for self-injury and conduct a content analysis and statistical tests exploring the following research questions: (1) What types of peer support exist and are common on TalkLife?, (2) How is the type of support sought related to the type of support provided in comments? And (3) What individual, message, and platform characteristics drive peer responsiveness on TalkLife? In the final empirical chapter of this dissertation I assess the relationship between self-injury outcomes (e.g., behavior, thoughts, urges, and ability to resist) and behavioral and linguistic patterns in naturally occurring log data from TalkLife. In chapter 4, I describe the results of analyses on self-injury outcomes (e.g., behavior, thoughts, intentions, and ability to resist urges) as a function of online engagement and language manifested in content. Specifically, I answer the following high-level 27 research questions: (1) What behavioral patterns are associated with NSSI behaviors, thoughts, intentions, and ability to resist?, (2) What language patterns are associated with NSSI behaviors, thoughts, intentions, and ability to resist? In addition, I pose a number of questions to probe potential mechanisms including the role of exposure to triggering content and use of affiliative language. In the concluding chapter (chapter 5), I summarize key findings from the preceding empirical chapters, describe implications for key stakeholder groups, and discuss future research directions. Self-Injury Terminology There are regional differences in the way scholars refer to and define self- injury (Muehlenkamp, Claes, Havertape, & Plener, 2012). While non-suicidal self- injury is the preferred terminology in the U.S. for deliberate acts of self-inflicted harm without suicidal intent, some scholars use the terms deliberate self-harm, self-harm, or self-injury. Research has shown that there are phenomenological differences between individuals who injure with and without suicidal intent, and these differences can influence methods of assessment and treatment (Muehlenkamp & Kerr, 2010). Thus, it is advisable to differentiate between cases with and without intent when possible (International Society for the Study of Self-Injury, 2018). Given the nature of the data analyzed in this dissertation, I was unable to discern whether participants had lethal intent with certainty. Therefore, I opted to use the more encompassing term “self- injury” in the remaining chapters of this dissertation. Conclusion Online communities may be able to meet a very large need for individuals with 28 stigmatized conditions, such as self-injury. Several gaps exist in our understanding of interactions on online communities and the effects of participation on mental health outcomes. The current dissertation attempts to address a number of these gaps through a thorough examination of use of a mobile peer support application, TalkLife, among individuals who self-injure. 29 CHAPTER 2 DESCRIBING USERS AND USE PATTERNS ON TALKLIFE For individuals with stigmatized conditions or experiences, online communities can provide a platform upon which to discuss sensitive topics and exchange information rather openly. Disclosures about mental health concerns such as depression (De Choudhury & De, 2014; De Choudhury, Gamon, Counts, Horvitz, 2013) and eating disorders (Chancellor, Mitra, De Choudhury, 2016; Chang & Bazarova, 2016), as well as difficult life events such as sexual abuse (Andalibi, Haimson, De Choudhury, & Forte, 2018) and pregnancy loss (Andalibi, Morris, & Forte, 2018) have been observed and studied in online environments. The advantages of seeking information or support online, versus offline, have been largely attributed to the sociotechnical affordances of computer-mediated environments (Walther & Boyd, 2002). In particular, the Hyperpersonal Model posits that features enabling users to post anonymously, the asynchronous nature of message exchange, and the ability to reveal certain aspects of the self while concealing others, work in concert to reduce self-presentational concerns and fears about stigma or judgement in response to such disclosures (Walther, 1996; Walther & Boyd, 2002). Indeed, first time help-seeking appears to be common in online environments and many people express comfort disclosing through online communities (Andalibi, Haimson, De Choudhury, & Forte, 2016; Frost & Casey, 2016). Given the potential benefit of participation in online communities, researchers have become interested in how to design systems that best facilitate disclosures and the exchange of social support. To do so, however, it is first important to understand 30 how people seek and provide support in existing platforms. To this end, research has shown that many factors including needs associated with the stigmatized condition, community norms, and the technical landscape of the platform (e.g., specific features) can influence a member’s experience, and potentially the benefits that they derive (Bliuc, Doan & Best, 2018; Chancellor et al., 2016; Carron-Arthur, Reynolds, Bennett, Bennett, Cunningham, & Griffiths, 2016). For example, topic modelling and language analysis of posts in online forums has revealed different topics (Carron-Arthur et al., 2016) and different types of support exchanged by condition (Sharma & De Choudhury, 2018). Platform affordances can also impact what people choose to disclose. Past work has shown that language features differ in anonymous versus non-anonymous posts (De Choudhury & De, 2014; Andalibi et al., 2016), and posts made in public versus private channels (Bazarova, Taft, Choi, & Cosley, 2012; Bazarova, Choi, Sosik, Cosley, & Whitlock, 2015). Moreover, certain online environments can encourage different types of support provision. Research shows that sensitive posts on Instagram, a primarily visual platform, have been met with emotional support (Andalibi, Ozturk, & Forte, 2017) whereas similar content generates more informational support on text-based platforms like Reddit (Andalibi et al., 2018). In sum, due to the heterogeneity of features and functions across platforms it is critical for researchers to consider both (1) how certain groups of individuals make use of these spaces and (2) what platform features may contribute to salutary exchanges. Self-injury is a behavior that individuals are often hesitant to disclose for fear of being judged or misunderstood (Evans, Hawton, & Rodham, 2005; Nixon, Cloutier, 31 & Jansson, 2008; Whitlock, Muehlenkamp, Purington, Eckenrode, Barreira, Baral et al., 2011). Indeed, in a college sample of individuals with a history of self-injury, just under 40% reported that no one knew of their self-injury (Whitlock et al., 2006). Despite low disclosure rates offline, research has shown that many individuals talk about, and seek support for, self-injury in online communities (Brown et al., 2018; Lewis & Michal, 2016; Whitlock, Powers, & Eckenrode, 2006). Understanding how individuals make use of online support communities is thus an important step towards developing interventions and designing platforms that best facilitate the exchange of information and support for these individuals. The present chapter contributes to this effort by describing how individuals who self-injure use an existing mobile peer support application, TalkLife. The overarching aims of this chapter are to describe how individuals who self- injure use TalkLife to speak about, and receive support on, self-injury. In particular, I explore the following questions: RQ1: Who uses TalkLife to discuss self-injury? RQ2: What are the normative use patterns? RQ3: What are the characteristics of content exchanged? In what follows, I describe some of the features of TalkLife before moving into descriptive analyses of the characteristics of users and use patterns. The Context TalkLife is free mobile application designed for young people with a variety of mental health concerns. The platform uses a crowdsourced peer support model to provide users with affordable and timely support outside of traditional therapeutic 32 settings. TalkLife’s functionality resembles that of many traditional online communities (e.g., illness-specific forums) in that it offers users a space to share and solicit information from other members and a means to show support or provide feedback to others. Beyond this, the application design shares many features with popular social media sites including the ability to follow other users and acknowledge posts with one-click reactions. From a user’s perspective, TalkLife is relatively simple to navigate. When a user creates an account, they choose a username and select whether they are on the site to get, give help, or both. This selection appears on the user’s brief biography along with their username and the amount of time they have been a member of the community. A more elaborate profile which is comprised of all posts, user photos, and other members who are in a user’s network (followers and followees) appears upon clicking on any member’s username from the main feed. In this profile, a section called “My Story” contains responses to prompts which allow members to become acquainted with one another (e.g., things that make me happy, favorite movies and tv shows, etc.). Often members share their age and diagnoses in this space, though they are not obligated to share any information beyond a username. When interacting with the application, members can click into other members’ profiles to learn more about them – much like they would on other social media sites like Facebook or Twitter. After a user account is created, users can solicit support by posting content to the main feed and respond to other’s posts by commenting or sending reactions (e.g., hug, support, OMG, heart). This feed is updated in real time with the most recent posts appearing at the top of the screen. Like on popular social media sites, network 33 connections – through following and being followed by other community members – are visible to others on the site. However, there are no constraints around following; users can follow anyone in the TalkLife community without initiating a conversation or otherwise verifying their connection. Several features allow users to customize their experience and diversify the way they express themselves. For example, before publishing a post, users must select a mood (e.g., sad, happy) and a category (e.g., self harm1, suicide, health, work) to accompany their content (See Appendices E and F for common moods and categories). Practically, these features (1) help users locate content that is relevant to their own struggles and (2) provide users with more information about the emotional state of the original poster, which may help facilitate more appropriate responses. By segmenting the data on TalkLife in this way, it is more easily searchable, much like hashtags on social media sites like Instagram or Twitter (McCosker, 2017). While many online communities allow for some degree of anonymity through the use of pseudonyms, TalkLife affords an additional degree of anonymity through the anonymous post. Past work has shown that anonymity can be desirable when disclosing issues around mental health or well-being (Walther & Boyd, 2002; Andalibi et al., 2018). Anonymous posts on TalkLife are displayed in the main feed along with other posts but are marked with the word “Anonymous” in place of the username. This feature is similar to the “throwaway” account on the social media site, Reddit (Andalibi et al., 2018; De Choudhury & De, 2014). 1 TalkLife is a UK-based company and uses the term self harm. I refer to self harm only when I’m discussing specific features TalkLife. 34 In addition to the social features of TalkLife, users can record thoughts, or archive exchanges (posts and comments), in a private space called the diary. While the potential benefits of such a feature have been noted in the human-computer interaction literature (Andalibi, Ozturk, & Forte, 2017; Chancellor, Mitra, & De Choudhury, 2016), to date relatively few empirical studies have assessed the effects of using an online diary as a feature of a social media platform (with exception to Lee, Kim, Yoo, Park, Jeong, & Cha, 2016). Finally, several layers of moderation exist on TalkLife to protect the community. These include a professional safeguarding team, moderators, volunteer supporters, and content-driven machine learning classifiers. The professional safeguarding team works to prevent escalation or risk on the site by reviewing and removing any content that is deemed inappropriate or abusive including graphic images or text on suicide and self-harm and posts that encourage negative behaviors. Moderators are responsible for flagging and quarantining this inappropriate content and sending it along for review by the safeguarding team. Finally, volunteer supporters (also called buddies) are highly engaged peers who have elected to undergo training in active listening, posing open questions, conveying empathy online, reflecting, summarizing, and self-care. These volunteers must apply, interview, and complete online modules to learn about providing peer support and identifying posts that could be distressing to other users. Volunteers are not allowed to give advice but are trained to provide passive support on the site. In sum, while TalkLife shares many features with other social platforms, it has some unique features of its own. The remainder of this chapter is focused on 35 understanding how users engage with the TalkLife platform, and how the platform facilitates the exchange of support. The chapter is presented in three parts. Descriptive statistics on users and user activity are presented in part one. The results of a statistical test to detect latent user profiles are discussed in part two. And in part three, I describe an exploratory analysis of language and linguistic features with the aim of understanding what types of content individuals are exchanging on TalkLife. Finally, key observations are summarized in the conclusion. Description of Data TalkLife provided me with access to a complete, de-identified, snapshot of their database drawn from each year since its launch in 2010. This dataset contains all of the metadata on site use including passive use and deleted content. While the full dataset was impressive in size—comprised of 432,958 users who had generated 5,460,890 original posts, and 15,468,730 responses to these posts (comments)—I refined the sample for analysis following recommendations in the literature (González-Bailón, 2013; Margolin & Markowitz, 2018). Specifically, I restricted the sample temporally and by my population of interest. First, it was important constrain the data to a period when the application remained relatively constant from the user’s perspective. As with many new applications, TalkLife went through major development and revision in the first years after launching. In an effort to control for noise introduced on account of early versioning, I spoke with developers to determine a period when there were few substantive changes to the site. Given that the last major feature added to the site (the diary feature) was released at the end of 2016 (released date: 11/8/16), I focused the 36 sample to activity from 2017 onwards. At this stage, the sample was reduced to 2,697,787 original posts and 9,167,991 comments. Secondly, the purpose of this study is to understand how individuals who self- injure use the TalkLife application. Thus, the sample was further constrained to self- injury related content in two ways. First, I made use of the TalkLife category feature. Recall that when posting to TalkLife a user must choose a category that best represents the content of their post. I retained all posts and comments during the aforementioned period that were categorized as self-harm. As a next step I wanted to protect against missing data that was pertinent to understanding how TalkLife is used by members who discuss self-injury. Self-injury can be related to a number of interpersonal or intrapersonal factors (Klonsky, 2009, 2011; Nock, 2009) and individuals struggling with self-injury may be hesitant to disclose their behavior even when they engage in help seeking activities (Whitlock et al., 2011). Thus, users may select a category signaling contexts related to self-injury rather than the behavior itself. For example, if injuring mitigates the negative feelings that arise on account of interpersonal conflict, a user may select the “relationships” category even though their post contains information relevant to self-injury behaviors. In order to minimize the risk of missing such content the sample was broadened to include posts that were flagged by previously validated self-injury classifiers. The final sample was comprised of all posts, linked comments, and behavioral data that were either classified or categorized as relevant to self-harm. This focal dataset included 444,343 original posts and 1,394,066 comments of which 187,392 posts fell the self-harm category, 321,284 were flagged by self-injury classifiers, and 37 64,333 were identified by both. A user dataset was created by extracting all user identification numbers that were included this sample. Importantly, due to the sampling method all users in the sample will have either posted a public or anonymous post or commented on a post that was related to self-injury. In total, the final sample included 105,504 users. Part 1: Understanding Users and Use Patterns User-Level Descriptives The first aim was to understand who uses TalkLife for support around self- injury. To this end, I describe some user statistics in table 3. The sample is comprised of 105,504 unique users, mostly female (63.5%), and around 20 years of age (mode = 18). The vast majority of users are standard users; however, the sample also includes administration, moderators, and buddies. I chose not to exclude these individuals from the descriptive analysis to avoid drawing artificial boundaries. However, to ensure that these users did not significantly influence the descriptives reported here, statistics were run both including and excluding these users. There were no significant differences. The majority of users (56.1%) indicated that they were on the site to get and give help, while fewer reported being there solely to receive (15.3%), or solely to give help (8.6%). The remaining users did not indicate a help type. Table 3 User Characteristics Characteristics Agea M(SD) = 20.5(6.2) Gender Female 66981 (63.5%) Male 32951 (31.2%) Other 5572 (5.3%) User Type 38 Standard user 105379 (99%) Administrator/Moderator/Buddy 11/32/82 (<1%) Help Type Here to help 9032 (8.6%) To get help 16160 (15.3%) Both 59063 (56.1%) Not specified 21249 (20.1%) Notes. Count (percentage) unless otherwise noted. a Due to the presence of unrealistic age values I examined age statistics with and without a filter restricted to ages 7 – 100 (n = 104067). A comparison between raw age and filtered age showed that they did not substantially differ so I chose to report statistics on the filtered age here. Duration. On average, users were on TalkLife for around 4 months (or 98 days) (See Table 4 average use patterns). This duration metric was computed as the interval between the date an account was created and the date of last engagement (e.g., last view, post, comment, or reaction). I felt that this was a rather robust estimation of use because it accounts for active and passive engagement, but I also recognize that users may take breaks from the application. Indeed, prior work suggests that participation in online communities for self-injury ebbs and flows with users actively engaged in the site when they need help and retreating from the site when it is no longer serving them (Lewis & Michal, 2016). It is not common for users to log-in or log-out of the application; therefore, I was unable to run an analysis to determine the frequency of breaks from the application. Passive versus active use. The distinction between passive and active use of social media and online communities is often invoked when discussing effects (e.g., Appel, Gerlach, & Crusius, 2016; Verduyn et al., 2015). Passive use includes logging in and looking at content, whereas active use entails building content or interacting with content or other users (Appel et al., 2016). In this sample, passive forms of use 39 appear to be more common than active forms of use. For example, the single most common activity among users was viewing posts (M = 928, SD = 5016, Mdn = 100). This was followed by a number of active forms of use such as one-click reactions to posts (M = 160; SD = 1006, Mdn = 28), commenting (M = 118, SD = 652, Mdn = 14), and creating posts (M = 18, SD = 80, Mdn = 4). Table 4 Average Use Patterns Characteristic M(SD) Mdn(IQR) Duration of use Days 121(261.5) 17(124) Years .29(.75) 0(0) Engagement Posts per user 18(80) 4 (10) Comments per user 118(651) 14 (44) One-click reactions 160(1006.5) 28(69) …support count per 25.9(184.2) 4(12) user …hug count per user 6(69.1) 0(2) …OMG count per user 1.9(23.66) 0(0) …heart count per user 37(17) 6(17) Comments on user 108.2 (559.5) 15(47) posts Reactions per user 3.07(2.38) 2.67(3) post Views of others’ posts 927.9(5015.9) 100(332) View of user’s posts 907(4659.6) 123(388) Embeddedness Users followed 6.7(47.8) 1 (4) Followers per user 7.5(36) 1 (4) Types of activity. Table 5 includes descriptives on user activity. The majority of users in the sample had posted at least one original post (89%, 93,919 users) or comment to an original post on the public feed (93%, 98,431 users). Original posts and comments were not operationalized to include anonymous posts, posts to the photo 40 feed, or deleted content. Thus, while most users posted with their chosen username, some users also elected to post to the public feed anonymously (15%, 15,926 users) or posted and subsequently deleted their post (19%, 20,504 users). Relatively few users posted photos (2%, 2629 users) or made use of the private diary (3%, 3116 users). Of the users who posted to the diary, a smaller portion (14%; 430) elected to post their diary content publicly. Finally, one-click responses (i.e. sending hearts, hugs, support) were used by nearly all of the individuals in the dataset. Only 35 users did not use a reaction of any kind. Table 5 User Activity (Percentage of Users Engaging with Features) Feature Total % Count Post 89% 93919 Comment 93% 98431 Diary 3% 3116 Anonymity 15% 15926 Deleted posts 19% 20504 Identified triggers 46% 48869 …sexual 15.5% 16458 …violence 13.7% 14559 …self-harm 20.7% 21908 …eating disorder/dieting 13.3% 14115 Note. Percentages are related to the entire population (n = 105,504) Network. TalkLife users have some agency over what, and who, they are exposed to through the structure of their social network. On average, users have small networks on TalkLife, following just over 6 individuals and being followed by 7; however, the mode for both followers and followees was zero, suggesting that this feature is not used frequently. Moderation. As a form of self-moderation, users can indicate the types of 41 content that they find triggering to avoid encounter them while scrolling through their feed. Forty-six percent of users selected at least one trigger group; and, of those, the most common triggers were related to self-harm (44.8%), followed by sexual content (33.6%), violence (29.7%), and eating disorders (28.8%). When a user identifies a trigger TalkLife automatically filters this content from their feed. Summary of User Descriptives In some ways the characteristics of users and use patterns on TalkLife are consistent with findings from past research. Similar demographics are represented in other online communities for self-injury. For example, Whitlock, Powers, & Eckenrode (2006) and Brown et al. (2018) report that a majority of users in the various online communities they reviewed tend to be female. While Whitlock et al., (2006) reports a mean age of 18 among users of online self-injury communities, Brown et al. (2018) finds a significantly younger group of individuals discussing self-injury on Instagram (M = 14.6, range = 12 - 21). This age range is also reflective of those who most often engage in the behavior, with adolescents (17% lifetime) and young adults (~13%) having the highest lifetime prevalence rates (Muehlenkamp et al., 2012; Swannell et al., 2014). Despite mental health being a sensitive topic, relatively few users in the sample choose to post anonymously on TalkLife. This is in contrast to findings in Brown et al. (2018), which show that the majority of user accounts that posted NSSI pictures to Instagram were anonymous. However, other work on online communities for mental health reports infrequent use of anonymity. For example, in one study less than 5% of users of the r-depression thread on Reddit posted anonymously via throwaway 42 accounts (De Choudhury & De, 2014). The difference in these two findings may be explained by site norms and the way anonymity is defined. For example, the use of pseudonyms is fairly common on Reddit, and less common on Instagram. Moreover, in Brown et al. (2018) an Instagram account was considered anonymous if it did not contain any personal identifiers (e.g., pictures, name), whereas in De Choudhury & De (2014), and on TalkLife, anonymity is a feature that can be selected at the post-level and is independent of identifiers in the individual’s profile. In sum, this finding may provide some insights into community norms around posting as well as how individuals engage in impression management strategies on TalkLife. Use of TalkLife also appears to be more heavily skewed towards passive behaviors. This preference for passive engagement, sometimes referred to as lurking, is consistent with past research on participation in online communities (Whitlock et al., 2006, van Mierlo, 2014; Nonnecke & Preece, 2000). Whitlock et al. (2006) find that between 14 and 65% of users in various communities for self-injury never post; and Nonnecke and Preece (2000) report that lurkers represent 45% of members in online health communities. Whether lurkers and active users accrue the same benefit of participation in online communities, is an important and debated topic (Mo & Coulson, 2010; van Uden-Kraan, Drossaert, Taal, Sevdel, & van de Laar, 2008). Research has shown that lurkers of an online community exchanging health information report feeling as informed and equipped to engage with treatment providers as active members, but they do not report enhanced social well-being (van Uden-Kraan et al., 2008), which is a critical element of recovery from mental illness and overall well-being (Davidson, Chinman, Kloos, Weingarten, Stayner, & Tebes, 43 1999). Finally, TalkLife users, on average, have very small personal networks. This suggests that rather than defining their social circle through an active following like on traditional social media where users must typically establish a link between yourself and another person to receive content (e.g., Facebook friends, Twitter followers); many TalkLife users engage predominantly through the main feed. In this way, TalkLife’s structure is more similar to online support communities which are commonly organized around different threads and content is visible to all users, than it is to traditional social media. Post-Level Descriptives As a next step in this exploratory analysis, I investigated activity at the post- level. There were 444,343 original posts in the sample, averaging about 59 words (range = 1 – 10352) in length. Self-harm was the most frequent category for posts (42%); however, posts in the self-harm category were oversampled when creating the focal dataset. As is evident in Table 6, others (13%; a social category), relationships (9%), and mental health (9%) were among the next most common categories for posts and, as such, these categories are among the most commonly associated with self- injury. In addition to selecting a category for posts, users can indicate the mood they were experiencing at the time of their posts. Notably, posts were more often associated with negative moods (66%; e.g., sad, heartbroken, anxious, angry), relative to positive (10%; positive, loving, supportive, happy) or ambiguous moods (24%; confused, numb, shocked, tired). 44 Table 6 Descriptives of Posts and Comments Count Total number of posts 444343 Total number of comments 1393066 M(SD) Mdn(IQR) Post length (words) 64.6(592.6) 29(52) Comment length (words) 18.9(26.8) 12(17) Comments per post 3.11 (5.67) 2 (4) Time to first comment 136 (306) 11226 (seconds) (345698) Reactions per post 3.2(4.2) 2(3) …support count 1.33(1.7) 1(2) …hug count .27(.85) 0(0) …OMG count 0(.02) 0(0) …heart count 1.59(2.21) 1(1) Views per post 29.8(50.45) 22(25) Count (% total posts) Anonymous posts 84228 (18.9% Diary posts 4981 (< 1%) …shared publicly 601 (< 1%) Deleted posts 124018 (25%) Frequent post categories …self harm 187392 (42.2%) …others 58844 (13.2) …relationships 41751 (9.4%) …mental health 39835 (9.1%) …my story 24083 (5.4%) …friends 17696 (4.1%) Frequent post moods …sad 90116 (20.3%) …heartbroken 41886 (9.4%) …tired 29231 (6.6%) …lonely 26375 (5.9%) …numb 25344 (5.7%) …anxious 22384 (5.0%) Notes. Percentages reflect percentages of total posts Responses to posts. In general posts received around 29 views and 3 comments, with the first response coming within the first 3 hours, though the mode 45 response time was within 37 seconds. There were 1,394,066 comments to posts in this sample, averaging about 18 words in length (range: 1 – 2299). In addition to written comments, users can show support on posts through one-click responses (e.g., support, hugs, OMG, hearts). Posts in the sample received about 3 one-click reactions and hearts were the most frequently used. Interestingly, around 27% (119,494) of the posts in the sample did not receive any comment. Of these, 29% (35,352) were subsequently deleted and a small portion were posted on the last day of data collection. By contrast, only 4% (17,771) of posts did not receive a reaction of any kind. In sum, this suggests that a substantial portion of posts do not receive tailored feedback through commenting, yet most receive some form of acknowledgement through reactions. Deleted content. Twenty-five percent of the posts in this sample were deleted (112,067) and 88.9% (99,669) of these were deleted by the original poster. A portion of these posts were deleted within a day of posting (39.5%, 39,402), suggesting that in all other cases individuals were reflecting back on their post history and may have been engaging in impression management strategies. Analysis of deleted content is controversial (Andalibi et al., 2017; Chancellor, Lin, & De Choudhury, 2016) however, given that a substantial amount of posts related to self-injury were deleted I felt that it was inappropriate to exclude them at this stage of analysis. Therefore, deleted content was not included in this descriptive analysis on the basis that I did not have access to personally identifying information and posts could not reasonably be traced back to the poster. Part 2: Defining User Profiles As is common in research on online communities (Chang, 2009; Rains, 2018), 46 significant variation in how users engage on TalkLife was evidenced. While the aforementioned description provides a broad overview of users and use of TalkLife, I aimed to tease apart use for a more nuanced understanding. To do so, I sought to identify different archetypes of users, characterized by different participation styles. In general, research has shown that participation in online communities follows a power law wherein the majority of users do not make any contributions (90%), a smaller portion make a few contributions (9%), and an even smaller portion (1%) make the majority of contributions (Rains, 2018; Carron-Arthur et al., 2016; van Mierlo, 2014). In a study of several online health communities, Van Mierlo et al. (2014) found that the top 1% “super users” contributed around 75% of the content (Nielson, 2014) and Carron-Arthur et al. (2014) found 83% in a replication on an online mental health forum. While contributions in the form of initiating threads or posts are the most commonly used metric to examine engagement in online communities there are others. A recent systematic review on the topic (Carron-Arthur et al., 2015) suggests that three broad metrics are used in extant literature profiling users including those related to the network, content, and activity (Carron-Arthur et al., 2015). In what follows, I characterize TalkLife members by using two of these metrics: network embeddedness (e.g., users followed and following) and activity-level (e.g., total user posts, comments, reactions). While I expect to see a similar power law distribution, the sampling method I employed will likely distort these proportions. Recall that the users in this dataset do not represent all users on TalkLife. The sample in the current study excludes individuals who have not made at least one self-injury related post or 47 comment. Data Analysis Plan Latent profile analysis (LPA) is an inductive, model-based technique used to identify subsets of observations that have similar values on key indicators (Pastor, Barron, Miller, & Davis, 2007). While similar to cluster analysis, LPA is a class of mixture modeling that accepts continuous input variables and is particularly adept at handling deviations from normality—an important consideration in the present case because I am using naturally occurring data on online behavior (Scott, Bay-Cheng, Prince, Nochajski, & Collins, 2017; Pastor et al., 2007). I performed LPA in the statistical program R, using the R package tidyLPA (Rosenberg, Schmidt, Beymer, Anderson, & van Lissa, 2018). Several indices were assessed to determine the best fitting model including: Log likelihood (LL), Bayesian Information Criteria (BIC), sample-size adjusted BIC (saBIC), Akaike Information Criteria (AIC), entropy, and the interpretability of clusters (Oberski, 2016). A better fitting model should contain smaller BIC, saBIC, AIC, and log-likelihood values and higher entropy (measured between 0 and 1) (Pastor et al., 2007). Per suggestions in the literature, I employed a bootstrapped likelihood ratio difference test (BLRT) as a final test of model fit (Pastor et al., 2007). The BLRT compares each k model with a model with k - 1 profiles to determine likelihood ratio model fit. A p-value of less than .05 suggests the tested model fits better than a model with one less profile. There was no missing data to consider. Results The latent profile analysis included five key variables: two of which related to 48 network embeddedness (e.g., number of users followed and following) and three of which reflected activity (e.g., post, comments, and reactions). Several parameterizations were considered for each model which either constrained or freely estimated variances and covariances. The model with unconstrained variances and covariances fixed to zero and the model with unconstrained variances and covariances resulted in the lowest BIC values suggesting better fit. Because key indices varied little between the two, I chose the prior, more parsimonious model, for the following analyses (Pastor et al., 2007; See Appendix A for a visual of model fit). The suggested cut off for this model was 4 profiles so I compared indices on 4 models with 1 to 4 profiles sequentially (Oberski, 2016; Pastor et al., 2007). Indices for overall model fit are reflected in Table 7. Log likelihood, BIC, saBIC, and AIC decreased as more profiles were added to the analysis. However, entropy decreased between three and four profile models, indicating poorer model fit. Thus, the three-profile model was selected as the best fitting model. The three-profile model had adequate BIC, entropy (0.98), log likelihood values, and good interpretability. Additionally, the mean posterior probability (whether an individual belongs to assigned group) was quite high for this model (average of 0.98). As a final test of confidence, I ran a BLRT. As noted in Table 7, the BLRT maintained significance up to the three-profile solution (p < .001). This shows that the three- profile model was an improvement over the two-profile model, and by contrast, the four-profile model was not an improvement over the three-profile model, further attesting the validity of the model choice. Key indicators for these three profiles (see Appendix B for a means plot of 49 profiles) are presented in Table 8. The first profile class represents 51% of the sample and corresponds to individuals with low engagement, while the second corresponds to moderate and third high engagement representing 38% and 11%, of the sample respectively. Indeed, a (thin) majority of users are least engaged, followed by a second group that contributes some, and a final group that contributes substantially. Table 7 Overall Model Fit One profile Two profiles Three profiles Four profiles LogLik 3394221.644 2232194.868 1980952.999 1872008.121 AIC 6788463.288 4464431.735 3961969.998 3744102.243 BIC 6788558.953 4464632.632 3962276.126 3744513.602 saBIC 6788527.172 4464565.893 3962174.429 3744376.947 Entropy 1 0.995 0.984 0.975 sample n / % 1 105504 83102 (78.77) 54115 (51.29) 41496(39.33) 2 22402 (21.23) 40143(38.05) 34887(33.07) 3 11246(10.66) 22526(21.35) 4 6595(6.25) BLRT (p- value) .001 .001 .001 1 The majority of users fell into the low engagement profile (51%). The low engagement profile is characterized by the lowest means or proportions on all indices. Members in the low engagement profile had the smallest ego network and were the least likely to have followers (M = 1.47, SD = .73) or to follow others (M = 1.41, SD = .74). These users also posted infrequently (M = 2.33, SD = 2.28), and had the least amount of feedback on their posts (comments: M = 7.45, SD = 7.97, reactions: M = 6.11, SD = 8.79). The mode for all of these indices was 0 indicating that a substantial portion of these users lurk the site rather than actively contributing to community content. Past work has similarly found that between 14 and 65% of users in online 50 communities for self-injury never post or post infrequently (Whitlock et al., 2006). Just over one third of users fall into the moderate engagement profile (38%). Members of this profile had a significantly higher level of engagement on TalkLife than those in the low engagement profile. These users had an ego network of about 4 followers (M = 5.41, SD = 3.96), and followees (M = 5.49, SD = 5.29), about 6 times as many posts (M = 13.1, SD = 13.47) and nearly 8 times as many comments (M = 54.9, SD = 55.68) and reactions (M = 57.7, SD = 69.89), as those in the low engagement profile. Finally, about 11% fall into the high engagement profile which is characterized by the greatest about of engagement and largest ego networks. Highly engaged users contributed 120 posts (SD = 100.78), 876 comments (SD = 1823.18), and 815 (SD = 1560) reactions on average. Additionally, these users had an average of 50 followers (SD = 65.46) and 39 followees (SD = 62.15). ANOVA and chi-square tests were run on continuous and categorical key and auxiliary variables to determine differences by profile type. Given the size of this sample, p-values are likely to be statistically significant even if they are not practically meaningful (Cohen, 1992; Harlow, Mulaik, & Steiger, 2013). Effect sizes convey the magnitude of the difference between groups without biases introduced through large sample size. Therefore, effect size estimations were calculated to gauge the strength of these relationships. Means and standard deviations of key variables are presented in table 8. ANOVA tests with pairwise comparisons showed significant differences across all three profiles on key variables: Followers per user (F(2, 105501) = 2.465e+04, p < 51 .000, η² = .32), users followed (F(2, 105501) = 1.61e+04, p < .000, η² = .23), posts per user (F(2, 105501) = 1.242e+04, p < .000, η² =0.1905), comments per user (F(2, 105501) = 1.026e+04, p < .000, η² = .16), and reactions per user (F(2, 105501) = 4801, p < .000, η² = .08). All effect sizes were in the small to medium range (Cohen, 1992). Means and standard deviations for auxiliary variables are also presented in Table 8. All tests were found to be statistically significant; however only duration and anonymous use reached a moderate effect size. In general, engagement seems to increase with time on the TalkLife. The low engagement profile is comprised of users who have been on the TalkLife for the shortest duration, around one month (Mda = 37.77, SD = 108.9, IQR = 1 – 22), compared to users in the moderate engagement profile who have been active on the site for about 4 months (Mdays = 142.7, SD = 240.17, IQR = 12 – 182), or users in the high engagement profile, who have been TalkLife members for over a year (M = 444, SD = 488.89, IQR = 122-582), (F(2, 105501) = 17180, p < .001, η² = 0.24). A chi-square test indicates that the proportion of users in the low engagement profile who post anonymously is much smaller than the proportion of users in the high engagement profile, 2X = 11301, df = 2, p < 000, Cramer’s V = 0.33. This finding may suggest that at higher levels of engagement, and presumably more self-disclosure on the site, users choose to conceal their identity on some posts as an act of identity management. However, it is not possible to know the motivations behind posting anonymously without conducting interviews with users. Finally, the proportion of users in the high engagement profile reporting being 52 on TalkLife to help is larger (16.3%) relative to other profiles (low = 5.9%, moderate = 9.9%), 2X = 2897.4, df = 4, p < .000, Cramer’s V = 0.12. The finding is important because the act of transitioning from solicitation to provision may mark meaningful progress towards recovery (Smithson, Sharkey, Hewis, Jones, Emmens, Ford, & Owens, 2011; Riessman, 1965) and reflects a trend observed in communities for self- injury to move from receiving support, to providing support for others (Whitlock et al., 2006). Table 8 Basic Descriptives for Three-Profile Model of Active Use Profile 1 Profile 2 Profile 3 Effect size Low Moderate High η² or n = 54115 n = 40143 n = 11246 Cramer’s V Variables defining latent profiles Embeddednessa Followers per user*** 1.47(.73) 5.41(3.96) 50.44(65.46) .31 Users followed*** 1.41(.74) 5.49(5.29) 39.43(62.15) .23 Activitya Posts per user*** 2.33(2.28) 13.1(13.47) 120.1(221.28) .19 Comments per user*** 7.45(7.97) 54.9(55.68) 876.7(1823.18) .16 One-click reactions*** 6.11(8.79) 57.71(69.89) 815.42(1560.06) .08 Auxiliary variables Agea** 21.0(6.62) 19.9(5.66) 20.1(5.92) .01 Genderb*** .04 …Female 34572(63.8) 25973(64.7) 6475(57.2) …Male 16957(31.3) 12019(30.1) 3975(35.3) …Other 2586(5.8) 2151(5.4) 835(7.4) UserTypeb*** .06 …Standard user 54115 40138 11126 …Administrator 0 1 10 …Moderator 0 0 32 …Buddy 0 4 78 HelpTypeb*** .12 …Here to help 3215(5.9) 3976(9.9) 1841(16.3) …To get help 9551(17.6) 5478(13.6) 1131(10.1) …Both 28354(52.4) 24026(59.8) 6683(59.4) …Not specified 12995(24.0) 6663(16.6) 1591(14.1) Durationa …days M(SD)*** 37.77(108.912) 142.71(240.17) 444.02(488.89) .25 In-linksa …Comments on user 8.94(12.51) 58.88(55.68) 763.07(1823.18) .17 53 posts*** …Reactions to users*** 3.71(4.84) 10.39(16.17) 62.24(182.60) .07 Passive usea ….views of other’s 67.13(158.45) 451.81(866.44) 6168.85(13514.02) .14 posts*** …views on users posts*** 72.48(109.08) 463.3(630.84) 6010.62(12598.33) .16 Diary useb*** 1309(2.4) 1208(3.0) 599(5.0) .05 Posted anonymouslyb*** 3925(7.3) 6782(16.9) 5219(46.4) .33 Notes. Means and standard deviations or counts and within profile percentages are reported in the table. * p < 0.05, **p < 0.01, ***p < .001 a continuous variable b categorical variable Summary of Profile Analysis The results of the LPA suggest that three distinct user types exist on TalkLife. Significance tests show that outside of the metrics used to build the model, one of the distinct characteristics of these user profiles is the length of time users have been on TalkLife. This is in line with observations in other online communities. For example, other work has shown that a highly engaged “core” set of users tend to have large influence over the site (Gruzd & Haythornthwaite, 2013). Furthermore, other research has shown that registration date (or time on the site) in online communities for mental health seems to correspond to user characteristics, including activity level (Carron- Arthur et al., 2016) The majority of users in this dataset who have been on the site for over 5 years fall into the high engagement profile, whereas those on TalkLife for a year or less fall into the low engagement profile. There are a few exceptions where individuals on the site for a short period of time have high activity; for example, 4537 users were on the site for less than a year and collectively had an average post of 80 (SD = 92). Fewer users were on the site for over 5 years while posting seldom (9 users; M(SD)posts = 54 2.2(1.48)). Moreover, the metrics of activity appear to conform to a power law. The minority of users make a majority of contributions; whereas the majority of users do not contribute or contribute infrequently. An additional observation from the LPA is that highly engaged users tend to make use of anonymity and indicate being on the site to offer help, at higher rates than moderate or low engagement users. These findings may suggest that over time these users develop an identity within the community and seek to preserve this identity through their posts. Moreover, it reflects a tendency of members of peer-support communities to transition from soliciting to providing support. Part 3: Examining Characteristics of Content In parts 1 and 2 I presented preliminary analyses describing TalkLife users and use patterns and answered two research questions: Who uses TalkLife to discuss self- injury? (RQ1) and What are the normative use patterns? (RQ2). I further examined profiles of engagement and found that, like other digital health communities, participation on TalkLife follows the power law distribution. I turn to an analysis of content in the final part of this chapter and answer the last research question: What are the characteristics of content exchanged? (RQ3). Specifically, I explore content through an analysis of the most common words and linguistic categories in posts and comments. Data Analysis Plan All posts and comments were pre-processed using Python programming libraries following guidance by Mehl and Gill (2010). Special characters, stop words, and non-English language were eliminated prior to analyses. I also corrected for 55 common shorthand, slang words and phrases (e.g., ty = thank you, thanx = thank you, idk = I don’t know). In doing so, the sample for language analysis was reduced to 442,259 posts and 1,373,784 comments by 105,039 users. Results N-gram analysis. To understand what users discussed in posts and comments, I first conducted a preliminary exploratory analysis of the most common unigrams and bigrams in the dataset. This n-gram analysis was conducted in R using the tidytext package (Silge & Robinson 2016). Results are presented in tables 9 and 10 (Please see Appendices C and D for wordcloud depictions of results). As is clear in table 9 unigrams related to feelings and emotion (e.g., feel, love, hate), being in distress (e.g., pain, bad, hard, die, kill), and relationships (e.g., people, friends, family, school) were common. Table 9 Most Common Unigrams in Posts and Comments Posts Comments word count word count feel 108783 people 100700 life 80211 feel 93528 people 78749 talk 81065 time 68521 life 79521 die 68456 time 55165 love 56678 love 50403 kill 47336 hope 34047 day 46720 care 32425 cut 42118 laugh 31824 thinking 40574 message 31281 friends 39182 loud 30044 hate 35513 hard 29560 talk 35396 person 29514 56 anymore 34284 bad 29347 friend 33885 yeah 28542 told 33255 strong 28359 bad 32678 stay 27830 stop 30095 day 26515 fucking 29779 happy 26407 happy 29140 friends 25876 The bigram analysis expands on these broad categories and contributes themes of urgency (e.g., right now, want die), support solicitation (e.g., need talk, want talk, someone talk, can help) and encouragement (e.g., feel better, stay strong). I also note the presence of language specific to self-injury (e.g., self harm, want cut) and language which may be indicative of the severity of distress (e.g., want kill, want die). Notably, while there is overlap in the most frequent words across posts and comments, words of encouragement appear to be more common in comments. Table 10 Most Common Bigrams in Posts and Comments Posts Comments word count word count feel like 32988 want talk 18591 just want 25184 feel like 16241 want die 24087 right now 12936 right now 19997 will get 12805 don’t know 16723 stay strong 12687 self harm 12637 don’t know 11432 best friend 8245 get better 11068 will never 7855 need talk 9615 even though 7775 can talk 7199 want kill 6911 feel better 7121 one day 6586 just want 7096 57 want cut 6397 can help 7053 did not 6397 need someone 7047 really want 6258 feel way 6536 kill kill 5989 someone talk 6148 die die 5847 self harm 5990 first time 5091 thank much 5954 last night 4944 one day 5839 need help 4803 let us 5558 just feel 4748 can get 5546 LIWC analysis. In an effort to examine language use more systematically, I next employed the Linguistic Inquiry and Word Count (LIWC; Pennebaker, Boyd, Jordan, & Blackburn, 2015), a widely used computerized language analysis software that returns words counts related to social, emotional, and cognitive processes. This dictionary-based approach is scalable, easily interpreted, and frequently used to analyze real-world datasets. Additionally, the language dimensions in LIWC are suitable for analysis of texts related to mental and physical health (Li, Mihalcea, & Wilson, 2018; Lyons, Aksayli, & Brewer, 2018). I extracted the most frequent 1000 words from posts and comments and organized them into LIWC categories following past work (De Choudhury & De, 2014). I report the percentage of these words that fall into various LIWC categories and provide paraphrased examples of the types of posts or comments in these categories (See Table 11). This list is meant to demonstrate the scope of the themes covered but is by no means comprehensive. Posts Of the top 1000 words from posts, affective expressions were the most common (25.9%) (e.g., love, pain, hurt, hope), with negative affect being particularly 58 salient (15.8%) (e.g., hate, bad, depression, scared, anxiety, cry), relative to positive affect (9.8%) (e.g., happy, hope, laugh, smile). Qualitatively, some of the posts with negative affect conveyed negative self-image or self-worth (e.g., I’m such a loser…no wonder no one cares), feelings of despair (e.g., I want to die; Let’s hope I die in my sleep; I should kill myself), and negative feelings about urges, or having engaged in behaviors (e.g., I’ve been feeling like hurting myself a lot lately; I should just stop eating…no one would care). Sixteen percent (16.5%) of these common words fell under the relativity category, which includes references to time, space, and time (e.g., time, day, stop, night, started, leave). These posts included references to past and present events (e.g., I just self-harmed for the first time and don’t know what to do; I cannot get over what happened last night…) and expressions of trying to stop harmful behaviors (e.g., I cannot stop hurting myself; I feel like I need to stop). Drives, a category which represents needs or motivations including affiliation, achievement, risk and reward processes, represented 13% of the words (e.g., lost, wrong, god, family, friends, worthless, girlfriend, sister). Among posts with drives were expression of intrapersonal struggles – feeling worthless, lost, or confused (e.g., I feel worthless) —as well as interpersonal tensions (e.g., My family is avoiding me; My family has been yelling at me) or feelings of loss (e.g., I lost the most perfect boy; Lost all of my friends…). Additionally, some of the posts in this category solicited companionship (e.g., I need a friend right now). Twelve percent of the most common words fall under the social dimension (e.g., people, friend, family, relationship). These posts again reflected tensions in 59 interpersonal relationships, as well as desires for relationships (e.g., I wish I was in a relationship, so I would not feel alone), solicitation of relational advice (e.g., What should I do—I’m in a relationship with…), and narratives about relationships (e.g., My family doesn’t understand me; My friend confided in me…). Eleven percent (11.2 %) of the top unigrams fell under biological processes (e.g., life, pain, heart, sleep, legs, head, eat, stomach). Posts including these words made reference to symptoms associated with self-injury, mental, or physical illness (e.g., I feel sick to my stomach; I have scars on my legs), poor body image or body insecurity (e.g., I wish my stomach was thinner; I hate my legs), and emotional and physical pain (e.g., I cut and I’m not feeling pain; words cannot express the amount of pain I feel). Finally, 10.4% referred to cognitive processes (e.g., feel, thinking, feeling, hope, reason, understand). These posts largely referenced feelings (e.g., I do not feel like talking to my friends; right now, I feel like hurting myself; I feel so numb; I feel suicidal again) as well as navigating relationships with family or friends when they find out about injury or illness (e.g., I hope my family will understand). In addition, many posts attributed causes to behavior (e.g., My mom is the reason I cut; Nobody understands me; You are the reason I changed…) or reflected hope about the future or encouragement to others (e.g., I hope everything will get better; hope everyone is doing ok). Table 11 Language Dimensions Corresponding to Top 1000 Words From Posts and Comments LIWC Post % Comment % Example words dimension 60 Affective 25.9 26.7 love, pain, hurt, hope expressions Negative affect 15.8 14.2 hate, bad, depression, scared, anxiety, cry Positive affect 9.8 11.5 happy, hope, laugh, smile Relativity 16.5 1435 time, day, stop, night, started, leave Drives 13 14.8 lost, wrong, god, family, friends, worthless, girlfriend, sister Social 12 13.4 people, friend, family, relationship Biological 11.2 9.3 life, pain, heart, sleep, legs, processes head, eat, stomach Cognitive 10.4 13.3 feel, thinking, feeling, hope, processes reason, understand Notes. Total percentages not equal to 100 because words could co-occur or not fit into these dimensions Comments Similar trends in language were noted in responses to posts. Affective expressions were again the most common (26.2%); however negative affect (14.2%) (e.g., kill, cut, hate, bad, pain, hurt) only slightly outweighed positive affect (11.5%) (e.g., love, happy care, hope laugh). Despite negative affect being common, these expressions often reflected the negative affect of the original post but were encouraging and conveyed empathy (e.g., I hate it when that happens; I’m sorry for all the hate you’ve gotten), offered advice (e.g., Would eating help take away that pain? Try drawing on yourself instead of cutting) or were aimed at deescalating the situation (e.g., You do not need to kill yourself; don’t kill yourself honey). However, some posts were not supportive (e.g., I do not care—I cut for fun; I want to cut, too). This is followed by drives (14.8%) (e.g., love, friends, stop, family, wrong) where commenters shared their own struggles (e.g., I’ve stopped before but then 61 something happens…; My family has found out before) and offered advice (e.g., Please make sure it stops bleeding…. Tell your family how you feel; You need to stop letting people do that to you…). Thirteen percent of comments contained words from the relativity dimension (13.5%) (e.g., time, day, anymore, stop, night). Some of these posts reflected commenters’ distress or feelings of giving up (e.g., I cannot live anymore; no one cares anymore), others were positive and encouraging (e.g., This made my day; happy that you’ve been clean for a day) and some shared practical advice or stories (e.g., One time I accidently mentioned something about my scars and then I was sent to a therapist; My parents punish me every time the school calls). The social dimension made up 13.4 % of top comment unigrams (e.g., people, love, friends, talk, told). These comments mostly conveyed relating to expressed struggles (e.g., I have several friends that do this); offered friendship (e.g., Would you like to be friends to share struggles?; Can we be friends?) and helped posters navigate relational issues (e.g., Some people are just rude…; maybe you should confront him around other people?). In addition, many of these posts appeared to offer companionship or a willingness to listen to and help others (e.g., Please do not do that, let’s talk; Is there someone you can talk to?). The cognitive processes (13.3%) dimension (e.g., feel, thinking, feeling, makes, hope) included commenters sharing feelings (e.g., I feel the same way; Yeah I know the feeling), helping the original poster make sense of their behavior or feelings (e.g., The pain makes you feel something instead of everything feeling numb…; I’ve been there if someone makes you feel not good enough then they are worthless, it’s not you; You may not believe it now but the way you’re feeling is totally normal) and 62 asking the poster to reflect on their feelings (e.g., What is making you feel that way?) Finally, comments related to biological processes (9.3%) (e.g., life, love, tired, pain, live, body) were also reflected in the top 1000 words for comments. These comments included guidance or validation on symptoms or body concerns (e.g., It’s ok your body just works that way; that’s because your body goes into survival model), crisis management (e.g., It sounds like so much blood, maybe you should get help; If it is bleeding find something to stop blood), and reflections of past struggles (e.g., I still have pain; I’ve had scars on my body for the past 5 years…). In addition, a number of these posts appeared to be positive and encouraging (e.g., Keep your head up hun!; It is your life, live it how you want to; life cannot be wasted) and expressions of love (e.g., Much love; Love you so much I feel the same; We love you). Summary of Language Analyses The breadth of topics reflected in both posts and comments suggests that members seek out and find advice on a variety of sensitive topics not all related to self-injury. While the inferences that can be made regarding findings from these exploratory analyses are limited, I can comment on general trends. Emotional disclosures were common in this sample and I find converging evidence that negative affect is more prevalent than positive affect, from both the moods attached to posts and the negative expressions in language. References to social or relational dimensions were also prominent. When looking at these posts qualitatively, it appeared that many users struggled with feeling alone and were in conflict with friends or family. This suggests that TalkLife may be filling a need for these users when they do not otherwise feel supported in their offline 63 lives. I also see examples of users opening up about injury for the first time or asking the community for advice about how to minimize the behavior, or the influence the behavior has on other aspects of their lives. Lastly, it appears that some users go to TalkLife when they are in, or trying to avoid, crisis situations. It will be critical for future work to investigate if and how individuals’ needs are being met in a timely manner. Self-injury can serve intrapersonal (affect regulation) or interpersonal (relational) functions (Nock, 2009; Klonsky et al., 2015) and the text analysis also highlights this dual emphasis on proximal concerns related to the behavior. Conclusion Several key observations have surfaced in this chapter and are summarized in the following section. First, the demographics of users on TalkLife appear to conform to patterns in extant literature on online forums for self-injury. TalkLife users are mostly female and largely fall within the adolescent to young adult range (ages 16 - 21). Secondly, user activity on TalkLife conforms to a power law. Many of the users in the sample do not actively engage on the site, or engage infrequently, while a small minority of users generate the majority of content. We also find that users in the high engagement profile appear to make use of additional features such as anonymity at higher rates. Engagement profiles are also related to total time (duration) users have been on the application—users in the high engagement profile have typically been active on TalkLife for a longer period than those in the moderate and low engagement profiles. The findings regarding user profiles have implications for design and intervention. Content generated by individuals in the high engagement profile is most 64 likely to receive attention and to be viewed by other members. Having been on the application for longer, these users might be important in maintaining TalkLife community norms and moving content, including potential resources, between networks. Thus, it could be beneficial to monitor content generated by these users both because it can provide feedback to designers about where the site is and is not addressing user needs, and it might be critical to maintaining a healthy recovery- oriented community. In addition, the findings suggest that many users of TalkLife appear to be distressed and seeking help. The preliminary language analysis reveals that users in the sample frequently share negative emotions. This finding is in line with past work which has shown that negative self-disclosure is particularly high in public channels on online health support groups (Yang, Yao, & Kraut, 2017), but in contrast to posts on social media sites, like Facebook, which generally express positive emotions (Bazarova et al., 2015). Given TalkLife’s emphasis on providing a safe space to share difficult life experiences the presence of negative emotion is not surprising. A predominance of negative and hopeless messages in absence of useful, tailored feedback could lead to participation risks. Future research should explore how members respond to one another when in distress. Are members validating difficult experiences in a way that encourages growth or is there a high degree of co- rumination? While it appears that members are exchanging support on TalkLife and the presence of supportive language is higher in comments than in posts, it will be critical to understand the dynamic exchange of support to evaluate the potential for the application to benefit its users. 65 Finally, the most salient dimensions to emerge from the LIWC analysis provide insights on which topics users feel comfortable discussing on TalkLife and the social, cognitive, and biological processes most often discussed in the context of self- injury. We note that while users discuss a broad range of topics, the ways they initiate feedback ranges from indirect disclosures or narratives about life circumstances to direct, and sometimes urgent, requests for help. Prior work has shown that solicitation style can be an important factor in how a post is received and responded to in online communities. Moreover, the response that users’ posts receive can subsequently affect their well-being. The next chapter (Chapter 3) will more carefully examine this relationship between solicitation style and response. Limitations While this chapter paints a broad picture of self-injury activity on TalkLife, the results should be considered in light of a couple of limitations. First, the sampling method limited my analyses to individuals who had either posted or commented on a post related to self-injury. Therefore, this sample did not include true “lurkers” – or individuals who may regularly view self-injury content and provide one-click support (e.g., reactions, hearts). Secondly, the language analyses presented here were preliminary and were based on common words without considering the context of these words. I sought to mitigate the impact of this limitation by providing quotes and examples of common themes within LIWC dimensions; however, these themes could be more rigorously examined in future work. 66 CHAPTER 3 PEER SUPPORT ON TALKLIFE Peer support has been identified as a key factor in recovery from a variety of mental and physical health conditions (Davidson et al., 1999; Colella & King, 2004). Referring to the exchange of support between individuals with experiential similarity (Thoits, 2011), peer support is based on the premise that peers with first-hand experience of an illness are particularly adept at addressing psychosocial issues and giving practical advice (Proudfoot et al., 2012). In line with this, a large body of literature links peer support groups to outcomes such as increased life satisfaction, improved coping skills and social function, and lower rates of hospitalization (Mead et al., 2001; Proudfoot et al., 2012; Galanter, 1988). Advances in technology have enabled individuals to access online venues for peer support (Kazdin, 2017; O’Leary, Bhattacharya, Munson, Wobbrock, & Pratt, 2017). Indeed, peer support services are among the most frequented health platforms online (Chang, 2005; Davison, 2000; Melling & Houguet-Pincham, 2013) and popular social media sites, such as Reddit and Twitter, are increasingly contexts where individuals discuss sensitive health topics among peers (Naslund et al., 2016; De Choudhury & De, 2014; Andalibi et al., 2017). However, evidence linking participation in online peer support communities to outcomes related to mental health and well-being remains limited (Houston, Cooper, & Ford, 2002; Powell & Clark, 2007; Lepore et al., 2008; Mehta & Atreja, 2015; DeAndrea & Anthony, 2013). Some research finds significant overlap in the benefits derived from exchanging online and offline peer support (Barak et al., 2006). For instance, participation in online 67 communities has been associated with self-reported feelings of empowerment, reductions in social isolation, and diminishment of mental health symptoms (Barak et al., 2008, 2006; Naslund et al., 2016; van Uden-Kraan et al, 2009; Johnson, Zastawny, & Kulpa, 2010). Yet, other work finds adverse effects of online peer communities including increasing identification with illnesses, normalizing symptoms, and promoting harmful coping strategies (Easton et al., 2017; Whitlock et al., 2006; Lewis, Mahdy, Michal, & Arbuthnott, 2014). Given the noted potential for online peer support to influence individuals’ experiences with mental illness, research assessing online factors which “tip the scale” towards positive or negative outcomes is especially needed. One of the most promising aspects of online venues for peer support is that they may attract individuals who are otherwise unlikely to engage in formal treatment or help-seeking (Naslund et al., 2017; Wilks, Coyle, Krek, et al., 2018). In a survey of a nationally representative adult sample, individuals with severe depression and those in serious psychological distress were over-represented among online help-seekers (DeAndrea & Anthony, 2013). Researchers acknowledge that adolescents, in particular, may feel more comfortable seeking information and peer support around sensitive topics like suicide, or self-injury, online (Suzuki & Calzo, 2004; Tate & Zabinski, 2004). In the present study, I focus on the exchange of peer support among individuals who struggle with self-injury, a common and concerning behavior that merits attention due to its prevalence, low disclosure rates, and the potential to result in fatal outcomes (Evans, Hawton, & Rodham, 2005, Nixon, Cloutier, & Jansson, 68 2008; Whitlock et al., 2011, Andover, Morris, Wren, & Bruzzese, 2012). Evidence suggests that many individuals who self-injure engage in help-seeking among peers (Michelmore & Hindley, 2012) and through online communities (Whitlock, Powers, & Eckenrode, 2006; Lewis & Michal, 2016; Lewis, Rosenrot, & Messner, 2012; Rodham, Gavin, & Miles, 2007). Such informal help-seeking is a recognized, and potentially a critical resource for these individuals. While prior work has examined, and theorized on, the potential outcomes of using online forums to discuss self-injury (Lewis et al., 2012; Lewis & Seko, 2016), there is a gap in knowledge around how individuals seek peer support in online communities and which types of support they receive (Lewis & Seko, 2016). Based on past research, the relationship between solicitation and response may be critical to understanding outcomes of participation. Prior work has shown that the response individuals receive to sensitive disclosures, like those of self-injury, can influence satisfaction with disclosures (Bazarova et al., 2015) and drive future disclosure behaviors (Jiang, Bazarova, & Hancock, 2011). Additionally, the amount of feedback individuals receive can be an important indicator of support; yet, to date, there is limited evidence for which factors contribute to these outcomes (Andalibi, Haimson, De Choudhury, & Forte, 2018). In an effort to address these gaps, I designed a set of studies aimed at understanding how individuals use TalkLife to seek information and support around self-injury from a peer support lens (for information on TalkLife please see Chapter 2). Specifically, I investigate (1) the prevalence of peer support types, (2) the relationship between solicitation styles and the type of support received, and (3) 69 factors that predict peer responsiveness. It is my hope that these findings can be useful in designing interventions to facilitate supportive exchanges and guide optimizations of existing platforms. Online Peer Support Individuals frequent online communities for a variety of reasons and engage in different methods to solicit support and information on these sites. Obtaining social support is one frequently documented motive for online engagement (Andalibi et al., 2016; Lewis & Michal, 2014). The literature generally identifies five key types of social support, including: emotional support (e.g., reassurance, esteem), instrumental support (e.g., material goods, services), informational support (e.g., advice, feedback), companionship (e.g., belonging, social), and validation support (e.g., feedback, social comparison) (Cutrona & Suhr, 1992). Of these, research has shown that informational and emotional support are commonly exchanged online (Wang, Kraut, & Levine, 2015; Wang, Zhao, & Street, 2014; Sharma & De Choudhury, 2018). Emotional support involves communicating care and empathy or validating someone’s experience. Receipt of emotional support has been associated with diminished feelings of loneliness or social isolation and increased commitment towards, and time spent, in online communities (Wang, Kraut, & Levine, 2015). By contrast, informational support is usually directive, provides information, or recommends potential resources. This type of support is particularly useful when individuals are looking for alternative coping strategies or tips regarding symptom management (Helgeson, 2003; Wang et al., 2012) and has been linked with increased coping skill-based efficacy (Wang et al., 2012). 70 Individuals can differ in their preferences for certain types of support. For example, prior theorizing suggests that when stressors are within an individual’s control, informational support is preferred; whereas, when individuals feel that their stressor (or condition) is outside of their control, emotional support is appreciated (Cutrona, 1990). In online support communities for self-injury, both emotional support and informational support are common; however, some studies show that emotional support and validation are disproportionately sought (Daine et al., 2013; Lewis & Michal, 2016; Lewis, Rosenrot, & Messner, 2012; Rodham, Gavin, & Miles, 2007; Whitlock et al., 2006). Solicitation Strategies Individuals engage in different strategies when seeking support online. The social support activation model suggests that support seeking can be approached in direct and indirect manners (Barbee & Cunningham, 1995). Direct support seeking includes asking questions or explicit disclosures of problems and requests for help, whereas indirect methods may simply allude to a problem without a clear request for support or advice from the community (Liu et al., 2017; Morrow, 2006). Studies of online forums for self-injury confirm that some members post indirect narratives about their struggles, while others engage in more direct exchanges about symptom management (Rodham et al., 2013; Lewis & Seko, 2016). Goals and Risks Goals associated with these direct and indirect methods can vary (Andalibi, Morris, & Forte, 2018). For example, indirect disclosures may be enacted for the purposes of self-expression, catharsis, personal record keeping, or finding similar 71 others, while also allowing the individual to maintain a level of ambiguity about their needs (Andalibi et al., 2018). By contrast, direct requests enable users to make their needs clear to the community but carry some risk of identification or further stigmatization (Omarzu, 2000). This may explain why indirect methods appear to be more common online (McKiernan et al., 2017; Barbee & Cunningham, 1995; Morrow, 2006), while direct methods often solicit a greater volume of supportive replies (Liu et al., 2017) and more relevant feedback (Bambina, 2007; Burke, Joyce, Kim, Anand, & Kraut, 2007). In sum, the method of solicitation can impact the type of support individuals receive. Relationship Between Solicitation and Response Identifying approaches to online support-seeking is important because these initial expressions affect the types and quantity of support received (Wang et al., 2015; Andalibi et al., 2016). In general, direct requests are most commonly associated with informational support, whereas indirect posts have been associated with emotional support (Wang, Kraut, & Levine, 2015; Vlahovic, Wang, Kraut et al., 2014). The optimal matching model of support further contends that the benefit of receiving support is contingent upon whether the request successfully recruits the type of support one needs (Cutrona, 1990). For example, in a study of an online cancer support community members reported being more satisfied when their requests for informational were matched with informational support, than they were when they received emotional support (Vlahovic et al., 2014). Moreover, Prior research has shown that matched support can have a greater impact on psychological well-being than the amount of support one receives (Reynolds & Perrin, 2004). Matched support 72 has been associated with improved coping efficacy, psychological adjustment, and satisfaction in online peer support communities (Andalibi et al. 2016, Cutrona, 1990). However, not all studies have found matching to be important. In qualitative work on a forum for self-injury, Smithson et al. (2011) found that matching was not critical— perhaps because the majority of community members expressed a need for emotional support. Additional Factors Contributing to Support In addition to the aforementioned dynamics around support solicitation, prior research has identified several other factors that influence the exchange of support online, and potentially the benefit derived from this support. A socio-ecological perspective which considers multiple, contextual layers of influence can be useful when investigating these additional factors at an individual, message, and platform level (Andalibi et al., 2018; Weiss et al., 2013). In what follows, I briefly outline some prior research on factors that impact participation in online communities, and the potential for this to influence peer support dynamics. Individual characteristics. There is significant variation in the way individuals engage online. Some individuals participate in online communities actively, by posting and commenting on other content frequently, whereas others prefer to consume content without engaging with others or publishing content of their own (Nonnecke & Preach, 2000; Van Uden-Kraan et al., 2008). A complex interplay of personality, individual motivations, and preferences are thought to impact engagement style and disclosure (Abramova, Krasanova, Wagner, Buxmann 2017). In online communities, participation generally follows a power law distribution wherein 73 a minority of users (1%) make the most contributions and the majority make far fewer contributions (Rains, 2018). In the context of online support this is important because an individual’s participation style can impact the amount of social capital they have and the amount of attention and responses their posts receive (Rains, 2018; Carron- Arthur et al., 2016; van Mierlo, 2014; Barak & Dolev-Cohen, 2006). Further, individuals who receive more attention may reap the most benefit from online support communities. For example, in a study of an online support forum for distressed individuals, active participation (the total number of posts and replies) was associated with greater reductions in distress in subsequent months (Barak & Dolev-Cohen, 2006). Gender has also been associated with supportive exchanges online. For example, some work suggests that it is more difficult for males to seek and obtain social support (Barbee et al., 1993). This may be because males typically engage in less self-disclosure (Wang et al., 2016) and are more likely to reciprocate self- disclosure when responding to other males (Barak & Gluck-Ofri, 2007). Males are also more likely to choose anonymous posting features when discussing sensitive topics, like sexual abuse, online (Andalibi et al., 2016). Similarly, the amount of time an individual has been part of the online community (user tenure) may impact their peer support patterns. For example, some research finds long-time members of online communities are more likely to engage in socialization of newcomers (Hseih, Hou, Chen & Truong, 2013) and that community commitment and contribution volume evolve over time (Nov, Naaman, & Ye, 2009a). Other studies, however, suggest that some aspects of community engagement decrease 74 with time due to boredom or changing motivations (Brandtzaeg & Heim, 2008; Nov, Naaman, & Ye, 2009b). This past work has mostly focused on crowdsourcing and information-sharing community, however. In online communities for illnesses or stigmatized behaviors, there are a number of other factors that influence both the amount of time users are in the community and their involvement, such as motivations, illness trajectory, and their own personal development (Nov, Naaman, & Ye, 2009a). In any case, user tenure has the potential to impact the visibility of members and the likelihood of their participation and their status. Other research shows that language use, and topics discussed, in online communities evolves with time. For example, Nguyen and Rose (2011) found that long-time members of breast-cancer message boards used highly informal language and community jargon and that newer members conformed to this vernacular over time. Moreover, the researchers could predict user tenure through the use of language features (Nguyen & Rose, 2011). Another set of researchers similarly found that language features could distinguish between cohorts of individuals in online communities, and that when individuals stopped following the language accommodation trajectory, this predicted their departure from the group (Danescu- Niculescu-Mizil et al., 2013). As individuals become more familiar with site norms, this can impact the support they receive (Nguyen & Rose, 2011). In sum, individual factors (including level of engagement, demographics, and the amount of time in a community) must be carefully considered when examining how users may benefit from online peer support. Message characteristics. Beyond direct and indirect strategies for soliciting 75 support, other characteristics of posts (e.g., message content, emotional valence) have been associated with community response. Language use, in particular, is seen as a powerful indicator of mental health status and has been used in many studies exploring outcomes related to participation in online health communities (Pennebaker, Boyd, Jordan, & Blackburn, 2015). For example, Chang and Bazarova (2016) examined language use in initial disclosures in an online pro-anorexia community and found that language in posts (e.g., stigma, biology, and family words) impacted the valence of response which further predicted the amount of stigma-related words in follow-up posts from the original poster. In this case, the authors describe that posts with language at odds with the goals of the community (e.g., to maintain eating-disorder behaviors) received responses with negative valence. Research aiming to identify or predict mental health outcomes has identified several dimensions of language including self-attentional focus through the use of first-person pronouns, and illness specific language such as cognitive processing or body words, as important in outcomes (Chung & Pennebaker, 2007; Chancellor et al., 2016; De Choudhury & De, 2014). Self-referential language has been associated with the degree of self-disclosure in a post and tends to increase reciprocity online (Barak & Gluck-Ofri, 2007; Wang, Burke, Kraut, 2016). For example, in a study of disclosure types, participants who read posts containing core disclosures (e.g., intimate details about an individual’s self-concept, beliefs, fears), relative to peripheral self-disclosure (e.g., name, demographics), exhibited higher levels of emotional involvement (reflected in greater use of emotion words) and more reciprocity (Pan et al., 2018). The presence of self-relevant language has also been linked to the volume of 76 comments posts receive. In a study of Facebook, Burke and Develin (2016) found that posts containing self-relevant emotion, in contrast to posts without self-relevant emotion, received a higher volume of comments. Another key component impacting how a message is received, and responded to, is the perceived seriousness of the content. The triage model of support implies that posts which express the most need should receive more support than those with less need or less serious content (Lepore, Glasser, & Roberts, 2008; Liu et al., 2017). In a recent qualitative interview study, researchers found that perceived seriousness was a key determinant in whether social media users responded to distressed posts (Chang, Whitlock, & Bazarova, 2018). When distressed posts reached a certain threshold, be that mentions of emergent medical situations, suicide, or self-injury, participants expressed being more likely to respond even if they had no prior relationship with the original poster. Indeed, posts disclosing recovery problems in an online community for alcohol use disorder, received more supportive comments, than those without such content specific disclosures. Similarly, in a study of self-injury images on Instagram, the severity of wounds was positively associated with the number of comments the post received (Brown, Fischer, Goldwich, Keller, Young, & Plener, 2018). A final factor that has proven to be important in the exchange of peer support is the emotional valence of expressions. In general, emotional posts often receive more feedback than posts without strong emotions (Liu et al., 2017), however some work has failed to find an association between affect and peer responsiveness (Lewallen et al., 2014). Emotional valence can impact the way in which users choose to express their support or acknowledge the poster based on site norms. For example, in the study 77 by Burke and Develin (2016) Facebook posts with negative emotions received more private messages and comments, but fewer likes, than posts without these emotions. Negative emotion in posts was also followed by more supportive words in comments (Burke & Develin, 2016). The pattern of these findings parallels results from De Choudhury and De’s (2014) study of a depression sub-reddit. Specifically, negative emotion in posts hindered karma (the equivalent of a like on Facebook or a reaction on TalkLife) but increased the number of comments in a sub-reddit for depression (De Choudhury & De, 2014). In sum, characteristics associated with the message itself including content, language, and valence of posts, can impact the potential for peer response. Characteristics of the platform. As online communities continue to evolve their technical affordances become more diverse and heterogeneous, making it important to assess how certain features facilitate supportive interactions. Anonymity is a common feature assessed in the context of online health forums and is seen as an attractive when disclosing sensitive information. Prior work has shown that message reception and response can differ by the level of post anonymity (Leavitt, 2015; Andalibi et al., 2016; De Choudhury & De, 2014). For example, in De Choudhury and De’s (2014) study of depression sub-reddit posts made from anonymous “throwaway” accounts received more one-click responses and emotional support, and fewer comments, and less informational support. Other work has examined the relationship between the number of features signaling distress and willingness to provide support in online communities (High et al., 2014). In a study of emotional bandwidth or the number of features which can 78 communicate information about affective states online, High et al. (2014) hypothesized that individuals would respond to high emotional bandwidth profiles with more support. However, they found that, on Facebook, this was only the case for females, people who perceived a sense of community, and individuals who preferred to exchange social support online. This finding not only points to the importance of assessing features of the platform but also highlights the need to account for individual differences. In sum, the foregoing review suggests that there are a number of factors to consider when studying peer support online. However, to date, many of these factors are underexplored in online activity around self-injury. The present study was designed to shed light on patterns of support in an online community of self-injuring individuals and identify factors that contribute to peer responsiveness in terms of volume of support or the types of support provided on a novel platform. Specifically, I pose the following research questions: RQ1: What types of peer support exist and are common on TalkLife? RQ2: What is the relationship between type of support sought and type of support provided in comments? RQ3: Which individual characteristics, message characteristics, and platform features drive peer responsiveness on TalkLife? Method and Study Design I conducted a three-phase study to answer my research questions. In the first phase, I characterize content on TalkLife through the lens of peer support literature by applying quantitative content analyses methods to posts and comments. I then describe 79 salient themes from these categories qualitatively. In the second phase, I investigate the relationship between solicitation and response type on TalkLife using logistic regression. Finally, in the third phase, I examine the relationship between posts and three aspects of peer responsiveness, including: comment volume, reaction volume, and the amount of time before a post receives a response. Results of these analyses are described at each phase. Data Sample Data for all phases of this study come from the mobile peer support application, TalkLife. A detailed description of TalkLife, and the full dataset, can be reviewed in Chapter 2. Analyses for the present chapter were performed on a random sample of posts and associated comments extracted from this larger dataset. Six random samples of 100 posts were extracted using the sample function in R’s base package. Twenty-two duplicates were eliminated from the sample, leaving 578 posts for analysis. Comments associated with these posts were also pulled for analysis. In total, the sample included 578 posts and 1321 comments from 1106 users. The majority of these users had one published post in the sample, but one user published 5 posts and 15 users published 2 posts. Three-hundred ninety-seven users published just posts, 555 users published just comments, and 154 users published at least one post and comment in this sample (See Figure 1). 80 D I ST R I B U T I O N O F P O STS A N D CO M M E N TS post comment post and comment 14% 36% 50% Figure 1. Distribution of posts and comments User characteristics. Individuals in this sample were around 22.42 years old (Mdn = 19, IQR = 6), mostly female (58%, N = 645; Male: 35%, N = 395; Other: 5.9%, N = 66), and had been part of the TalkLife community for less than a year on average (278 days; SD = 307, Mdn = 166, IQR = 324). The sample was predominantly comprised of standard users, although 2 administrators, 2 moderators, and 7 volunteer supporters (buddies) were included. The majority of these users indicated that they were on TalkLife to get and give help (56.6%, n = 627) but some users indicated being on TalkLife only to provide help 16.4% (N = 182) or only to receive help (12.3%, N = 136), all others did not specify their help type. These proportions resemble those reported for the larger dataset in chapter 2. Phase 1: Characterizing Peer Support on TalkLife Data Analysis Plan 81 To address the first research question (RQ1: What types of peer support exist are common on TalkLife?) I characterized the types of solicitations and responses on TalkLife in a quantitative content analysis. I describe commonly discussed topics based on a qualitative account of these codes. The methodological approach for developing and applying codes are outlined below. Posts. A semi-open coding process was employed to develop a codebook for posts. First, I thoroughly reviewed extant literature on peer support and familiarized myself with the data. Based on this review, an initial codebook comprised of 6 codes was developed (e.g., direct, indirect, emotional support, informational support, urges, relapse) primarily based on the Social Support Activation model (Barbee & Cunningham, 1995) and empirical studies of online peer support in similar populations (e.g., addiction; Liu et al. 2017). The author and two trained research assistants coded a batch of 100 posts separately and then met to discuss each post and code applied. At this stage research assistants were instructed to make note of any themes in the data that were not addressed by the existing codebook. Several important observations were gleaned from this process and led to a new coding structure. Specifically, while it was possible to discern whether direct posts were soliciting emotional or informational support, it was not possible to discern type of support for indirect posts. Indirect posts differed greatly in the valence, however. In an effort to obtain granularity, the final codebook was refined according to these observations (See Table 12). Thus, the coding scheme was hierarchical (See Figure 2 for visual). Posts were first coded for solicitation type: direct or indirect. Direct posts we further coded for whether the poster primarily 82 expressed a need for informational or emotional support. Indirect posts were further coded for valence (e.g., positive, negative, ambiguous). Finally, we coded for the presence of self-injury specific themes including mentions of urges and relapse. PHASE 1: PHASE 2: Solicitaiton style Self-injury themes direct indirect urges relapse emotional positive informational negative ambiguous Figure 2. Hierarchical coding scheme for posts Coders were familiarized with the codebook and practiced discerning and applying codes in four consecutive training weeks. After this, coders were given a set of 100 posts to code independently to test for interrater reliability. Sufficient interrater reliability scores were met for all 9 codes. Overall Cohen’s kappa was .84, with moderate to strong kappa statistics for all independent codes (range: .74 - .88). Next, coders went through and applied codes to the remaining posts. After each 100-post batch, coders met with the author to discuss discrepancies and the author made final decisions about reconciliation. Comments. Comments were also coded with a semi-open coding process. I developed a codebook containing 4 codes (informational, emotional, companionship, and conversational) based again on the social support literature (Cutrona & Suhr, 1992), empirical studies (Wang et al., 2015) and familiarization with the data. The author and two different research assistants coded a batch of 100 posts separately and 83 then met to discuss each comment and code. During the training period, 4 additional codes were introduced as they captured dimensions not represented in the existing codebook (e.g., gratitude, clarification, follow-up, unsupportive). The final codebook consisted of 8 codes (See Table 13). Unlike posts, comments were not coded in a hierarchical fashion and were not mutually exclusive. After three consecutive weeks of training (including independent coding and collective discussions), coders were given a batch of 250 comments (corresponding to 100 posts) to test for interrater reliability. Overall Cohen’s kappa was strong .90 and as were kappa scores for all independent codes (range: .84 - .96). Next, coders went through and applied codes to remaining comments. After each batch (ranging from 200 – 400) coders met with author to discuss discrepancies and disagreements were resolved by the author. Findings Codes, code descriptions, and prevalence rates for posts and comments can be seen in Tables 12 and 13. I discuss results for the coding of posts and comments separately below and then provide a qualitative account of commonly discussed topics within these codes. Posts. Indirect posts were the most common post type in this sample (81.1%). The majority of these indirect posts were of negative valence and included references to feelings of distress or sadness (59.3%). Some posts in this category appeared to indirectly allude to a need for advice (e.g., “I just don’t know what to do…”), whereas others presented as more confessional (e.g., “Want to cut so bad hoping admitting it will take it away but I can literally envision it”) or contained sensitive disclosures 84 without a clear solicitation of advice (e.g., “Let me die,” “Took too many pills. Scared now but if I do not care either”). Posts documenting struggles with self-injury (e.g., “Every time I am left alone for more than a half an hour I end up cutting and my fiancé works 72 hours weeks with me at home alone”), body image (e.g., “I am so fat, ugly, stupid, I feel so empty. I do not want to fucking be here anymore… I want to cut”), and suicide (e.g., “Fuck my life I failed suicide again. The hospital let me go once they dealt with all the meds in my system. I am going to try again but with a rope this time”) were also noted in this code category. Relative to indirect posts with negative valence, posts with positive valence were less common in this sample (4.5%). Some of these posts referenced progress in regard to behavioral cessation (e.g., “I have not cut for 4 days,” “2017 goal is to not self harm and I’ve been doing amazing!”), conveyed encouragement to others (e.g., “This post is for anyone who is having a difficult day…,” “You deserve to be happy. You have people who care about you.”) or were social in nature (e.g., “I’d like to be your friend,” “3 tips for a happy morning: listen to happy tunes….”). About 19% of the posts included direct solicitations. Of these posts, requests for emotional support were more common than requests for informational support (11.4% in full sample or 60% of direct posts). In some of these posts, individuals asked for support in response to having self-injured (e.g., “I am cutting and I need someone to talk to,”) or for fear that they may self-injure in the future (e.g., “I really want to self harm and need someone to talk to,” “All I can think about is cutting. I have never done it before but my mind just wants me to suffer but I know I will regret it someone help”). Posts mentioning suicide or suicidal ideation were also present in 85 this category (e.g., “My friends, do not leave me tonight. I feel like killing myself tonight.”). Many posts solicited advice about personal experiences (e.g., “I finally reached out to my friends after six months of hospitalization and therapy. I am so scared how they will respond back I was so selfish then. Can someone please give me some piece of mind?”). Table 12 Post Coding Scheme and Percentages of Codes in Dataset Code Brief description % (count) posts with code applied Direct Direct solicitation of support. This might include indicators such as a question mark or "please help" 18.8% (109) Informational Direct solicitation of information of advice about self-injury, coping, relationships, or other mental health concerns 7.4% (43) Emotional/ Direct solicitation of emotional or companionship support. Companionship This may include a poster asking for someone contact them 11.4% (66) or asking for encouragement, validation. Indirect Any post that is rhetorical or otherwise not explicitly asking for information, emotional, or informational support 81.1% (469) Negative Post that contains a self-disclosure which has negative valence or conveys a negative emotion state. 59.3% (343) Positive Post that contains a self-disclosure which has positive valence or conveys a positive emotion state. 4.5% (26) Post that contains a self-disclosure which has no clear Ambiguous negative or positive valence or has both positive and negative 17.3% (100) valence. Posts that mention a strong desire to self-injure. This should Urges mention the action of self-injury not just a passing reference 9.1% (53) to missing SI or the relationship someone has with SI. Relapse Any post that alludes to or mentions having relapsed or concern about relapsing. 2.5% (15) A smaller portion of posts asked for specific information or advice regarding symptoms or management of emotional states/relationships (7.4% total posts, or 40% of direct posts). Among posts coded as direct requests for informational support, many users referenced transitions into new life events (e.g., dating, changing schools, difficulties in interpersonal relationships). Other posters asked about coping strategies 86 (e.g., “I’m getting flashbacks a lot lately, how do you cope with them?”), for advice about navigating professional help (e.g., “I know I need to go to therapy but honestly I am so anxious about it. I have suicidal tendencies and I have tried to commit suicide twice… if I tell a therapist about this does anyone know what would happen in this type of situation?”) or disclosing struggles related to self-injury (e.g., “How can I tell someone that I self-harmed because I literally have no one”). Some posts requested medical advice in times of crisis (e.g., “So I overdosed on meds. I was fine until my ribs started hurting so much and I am not going to die but my ribs hurt so I took Motrin … any advice? It hurts so much I think I am going to throw up?”) or about the consequences, or practices, of self-harm (e.g., “Is it possible that after I always cut on the same place it will not bleed anymore?”). A small portion of these posts referred to tips on how to engage in negative behaviors (e.g., “how do I trigger?”, “What is the cheapest way to kill myself?”). Mentions of urges (9.1%) and relapse (2.5%) were relatively infrequent. Posts mentioning urges often conveyed a strong desire to self-injure (e.g., “I want to cut right now but my sister is in the room so I guess I will wait,” “I need to cut!”) or an inability to focus on things other than the behavior/practice (e.g., “I cannot stop thinking about suicide”). Posts alluding to relapse took two forms: announcing relapse to the community (e.g., “I am so weak after 4 month clean I broke and I relapsed so bad I had to go to the hospital,” “I am now down to 0 days clean,” “I was two weeks clean of self harming until yesterday”) or expressing concerns about relapsing soon (e.g., “4 years ago today was the day I took my first major overdose and I am so close to relapsing without even realizing the date”). 87 Comments. Emotional support was the most common type of support observed (33.7%), followed by informational support (7.8%) and companionship support (6.3%). Comments coded with emotional support provided posters with validation (e.g., “I know how you feel and I am always here,” “I’ve been going through a really similar situation as well…”), expressed shared struggles (e.g., “Nothing emotional is easy and I’ve been there,” “That happens to me like every time”), condolences (e.g. “I am sorry to hear that”) and conveyed encouragement (e.g., “Sending you the warmest virtual hugs. Do not let the past define you. Stay strong and keep moving forward,” “Then today is the day to get clean!”). Many comments encouraged highly distressed posters who were in crisis (e.g., “Please do not give up”, “there is never a reason to take your own life—talk to me!”). Comments acknowledging the original poster’s progress were also evident, but less frequent (e.g., “6 years is a big accomplishment!”). By contrast, companionship support was noted in 6.3% of comments. These comments included references to the commenter’s availability to talk or listen (e.g., “hey you want to talk?” “Feel free to message me if you ever want to talk”) without actually providing support in the comment itself. These posts were often followed by, or in conjunction with, provisions of emotional support. Comments providing informational support (7.8%) included advice on practical matters related to symptoms (e.g., “If you can get out of the house when you feel like you might start to cut yourself. If you stay out …it might help you get your mind off of it,” “take a pen and try to draw on yourself instead”), behavior (e.g., “Oh do you want to throw up maybe? You might od!”), as well as advice on how to change 88 life circumstances (e.g., “Sorry for your friend, you should get help from child support,” “if you’re an introvert then being around people won’t help,” “I have a service dog. He really helps with anxiety. Maybe that is something you could look into…”). While many comments encouraged members to continue to reach out on TalkLife, a number of comments in this category encouraged posters to talk to someone beyond the TalkLife community (e.g., “You need to talk to someone…”, “A doctor is the only one really qualified to deal with an overdosed person”). The remaining codes referred to comments that did not explicitly express support. For example, conversational comments made up 19.6% of total comments and were those that appeared to establish rapport between community members but did not contain a type of support in, and of, itself (e.g., “good one brother,” “it’s a pretty good song!” “welcome to the family”). Clarification, or instances when the original poster provides more information or context for potential commenters, were also quite common (22.3%) (e.g., “So, I should not be beating myself up about this? I just needed to get it off of my chest,” “I will get in trouble if I wander halls”). Finally, follow up comments often included a question directed at the original poster (e.g., “why?” “why do you say so?” “How do you know?” “Have you talked to doctors?” By contrast, unsupportive comments (8.9%) and comments with gratitude (<1%) were rare. Table 13 Comment Coding Scheme and Percentages of Codes in Dataset Code Brief description %(count) comments with code applied 89 Emotional Comments that provide individuals with validation, social integration 33.7% (451) or helps individuals experiencing “distress, uncertainty, stigma.” This includes offering empathy and general shared experiences. Conversational Comments that establish rapport with others, comradery, but do not 19.6% (263) cleanly fit in line with emotional support. Informational Comments that offer information to the original poster. These 7.8% (105) comments can help with decision making, coping, cravings (tips on how to heal wounds, therapy, or self harm methods, discussion board rules, relationship advice, reframing a situation, offering a new perspective etc.) Companionship Comments that involve the support provider describing availability to 6.3% (84) provide contact beyond the discussion board (private message) or to talk on the discussion board. Gratitude Comments from the original posters expressing gratitude or 3.8% (52) acknowledging (heart emoji) commenters Clarification Comments from original poster meant to provide more information 22.3% (299) or encourage more conversation. Follow-up Comments that ask the original poster for more information or seek 8.9% (119) context from the original poster Unsupportive Comments that are unsupportive or encourage negative behaviors. <1% (11) Notes. Percentages do not equal 100 because codes could co-occur Summary of Content Analysis As a whole, findings from phase one provide insights into how individuals express themselves and exchange support on TalkLife. In terms of solicitation style, members of TalkLife engaged in indirect solicitations more often than direct solicitations (Liu et al., 2017). This finding is consistent with prior work on online support-seeking among individuals with stigmatized conditions (Barker, 2007). For example, in Liu et al.’s (2017) study of an online community for alcohol use disorder, indirect posts were around 6 times as common as direct posts. However, other work has found a great deal of direct support seeking on Reddit among individuals who have experienced sexual abuse (Andalibi et al., 2016). The disproportionate use of indirect strategies on TalkLife may be attributed to several factors. First, past work has shown that the structure of the online community, 90 including how visible the audience is, can play a critical role in disclosure practices (Bazarova & Choi, 2014). The functional model of self-disclosure on social media asserts that people consider the affordances of online spaces, including the directedness of certain channels and network visibility, when deciding what and how to disclose (Bazarova & Choi, 2014). On TalkLife, all posts are made to a public feed and have communal visibility. This open design and lack of audience cues may make it difficult for members to disclose openly and ask direct questions about their condition. Indeed, past work shows that audience diversity complicates disclosure (Kramer & Haferkamp, 2011). The category feature on TalkLife allows users to potentially narrow their audience; however, proper use of this feature is contingent upon other users setting up filters appropriately. Therefore, even when using this feature a user’s audience can be diverse and unknown. Secondly, users may seek the attainment of different goals with indirect posts. For example, past work has shown that social media users sometimes post for intrinsic purposes such as to keep a personal record (Vitak & Kim, 2014). I document this type of record-keeping in the coded data. Individuals also use indirect disclosures as a strategy to solicit support while only hinting to their condition or need (Andalibi et al., 2018; Buehler, 2017). For example, prior work on emotional support seeking through Facebook shows that people often use indirect strategies such vague-booking to solicit support when perceived costs of revealing needs are high (Buehler, 2017). Vague- booking refers to posts that “contain little actual clear information but are worded in such a way to solicit attention and concern from readers” (Berryman, Ferguson, & Negy, 2018, p. 308). This type of post made up a small, but non-trivial, portion of 91 those classified as indirect. Indirect posts often included sensitive disclosures about life experiences and emotional states with no request for help. Sometimes these posts were relatively short and took on a confessional tone, whereas other times the posts were lengthy and recounted a detailed narrative about an aspect of the poster’s experience. In the prior case, I find evidence that TalkLife members use the application both to document perceived set-backs and express feelings of loneliness or hopelessness, and to celebrate successes in their life, including abstinence or periods of time without injuring or using. Prior research has similarly noted a disproportionate presence of hopeless messages in online activity related to self-injury (Seko & Lewis, 2017; Lewis & Baker, 2011; Lewis, Heath, Sornberger, et al., 2012) and high co-occurrence of indirect solicitations and disclosures about loneliness (Andalibi et al., 2016). Further, Rodham et al. (2013) note the presence of confessional rhetoric in posts and a motivation to use self-injury forums to share turning points or events in a space where others could bear witness. Posts with longer narratives may have served the purpose of “venting.” In fact, in some cases, posters acknowledged a need to vent. In past work, these types of posts have been referred to as sharing one’s story or story-telling (Andalibi et al., 2016). The benefit of sharing one’s story has been noted in studies of self-injury recovery online and this type of sharing might assist in creating a coherent narrative of their struggles, as well as to develop identity (Deering & Williams, 2018). Notably, the extent to which authors had the audience in mind at the time of posting was unclear in many indirect posts. This didactic use of online forums for self- 92 injury is not uncommon – in fact, some researchers suggest that a key goal of indirect posts may be more for expression than exchange (Rodham et al., 2013). This is important because while some work suggests that posting itself can be cathartic (Deering & Williams, 2018), outcomes related to well-being are associated with the disclosure response cycle – particularly the type and volume of support an individual receives (Andalibi et al. 2018). It may also be the case that posting in a public space makes users feel that they are no longer experiencing their condition, or circumstances, alone. When directly seeking support, most users sought non-specific emotional support, or companionship, due to feelings of loneliness or hopelessness. Indeed, emotional support was exchanged at a higher rate than both informational and companionship support. These posts were often brief and simply asked the community if anyone was available to talk or could provide them with affirmation. Past work similarly finds that individuals sought validation for their NSSI experience more commonly than seeking answers to specific questions about self-injury (Lewis et al. 2012). While a number of posts discussed self-injury or struggles related to mental illness, direct solicitations also included topics outside of this such as music preferences and relationship challenges. This topic diversity has been acknowledged in online communities for other conditions (Andalibi et al., 2016; Andalibi et al., 2018; Prescott et al., 2017). While the benefits of exchanging emotional support can include enhanced commitment towards, and feelings of social integration in, online communities (Wang et al., 2012), past work suggests that oversharing of negative emotions can lead to co- 93 rumination or emotional or behavioral contagion (Easton et al., 2017; Takahashi et al. 2009). Thus, while it seems promising that there is a rich exchange of emotional support, it will be important for future work to examine these emotional exchanges in greater detail and over time. Finally, while many posts contained explicit mentions of struggles with self- injury including those with addictive language as well as concerns around the consequences of self-injury, there was only a small proportion of post mentioning urges or relapse. This may be an artifact of the coding scheme. For example, posts such as “I want to die” or “I want to kill myself” were not coded as urges because it was not possible to discern the impulsivity or strength of the desire without further context. Similarly, posts mentioning self-injury independent of urges (e.g., behavior, thought, plans) were not captured in their own category. Prior work has shown that references to death are associated with suicide risk (O’Dea, Larsen, Batterham, Calear, & Christensen, 2017; Cheng, Li, Kwok, Zhu, & Yip, 2017). Further, phrases such as “want to die” and “want to commit suicide” have been correlated with increased odds of suicidal ideation, self-harm, suicide plans and attempts in blog posts (Ren, Kang, & Quan, 2016). In sum, while I did not conduct a thorough analysis posts mentioning death in this dataset, they were quite common and merit attention in future work. Phase 2: Match Between Solicitation and Response The analysis described above provides an understanding of the solicitation styles and types of support in the sample, as well as prevalence rates of these different dimensions. While there is a great deal of disclosure and support seeking on TalkLife, past work indicates that the benefit of peer support is contingent upon a match 94 between the type of support sought and received (e.g., optimal matching) and the amount of support individuals receive. In the next section I explore the relationship between the solicitation and response and address the second research question (RQ 2: What is the relationship between type of support sought and type of support provided in comments?). Data Analysis Plan To explore the relationship between the type of support sought and type of support provided, logistic regression models were run in the statistical software program R. Logistic regression is most appropriate when dealing with categorical predictor and response variables. In the present case, I explore whether the presence or absence of a solicitation type is associated with the presence or absence of a comment type. Significance is reported in terms of incidence rate ratios (IRR) and confidence intervals (CI). Incident rate ratios refer to the exponentiated coefficients. When interpreting IRR for binary variables, posts belonging to the category (e.g., direct posts) will have an incidence rate for the dependent variable (e.g., informational support) equal to IRR times that of those that are not in that category (e.g., indirect posts). Because comments were nested under the original post, a random effect of post was included in all models. Results First, I explore the association between direct or indirect posts and emotional or informational support in comments. I find that direct posts were more likely to receive informational support while indirect posts were more likely to receive emotional support. Specifically, for direct posts, the odds of receiving informational 95 support are 2.14 times (CI: .912, 5.335) as large as the odds of an indirect posts being receiving informational support (N = 1321, p = .08), however this relationship did not reach conventional levels of significance. For direct posts, the odds of receiving emotional support are .49 times (CI: .285, .853), or about 50% less than the odds of an indirect posts receiving emotional support (N = 1321, p = .01). In other words, it appears that direct posts were more likely to receive informational support, and indirect posts were more likely to receive emotional support. As a second step in analysis, I explored whether posts coded as direct requests for emotional support received emotional support, and posts coded as direct requests for informational support received informational support. Contrary to optimal matching, I found that for direct emotional posts, the odds of receiving emotional support are .53 times (CI: .257, 1.069) smaller than the odds of another type of post receiving emotional support (N = 1321, p = .07). However, for direct informational posts, the odds of receiving informational support are 5.84 times (CI: 2.01, 20.48) larger than the odds of another type of post receiving informational support (N = 1321, p = .002). This suggests that TalkLife users are more discerning when giving out informational support and are more liberal in their distribution of emotional support. There were no significant relationships between valence of posts, or mentioning urges or relapse, and receipt of informational or emotional support. Summary Findings from the analysis on solicitation and response types reveal two things. First, it appears that direct posts are met most frequently with informational support, whereas indirect requests are met with emotional support. This finding is aligned with 96 past literature (Wang et al. 2015). For example, in a study of an online cancer support group, Wang et al. (2015) found that informational support was most common when members asked questions, whereas self-disclosure was met with emotional support. Further these effects were partially mediated by perceptions that the poster was seeking that type of support (Wang et al., 2015). That is, the community evaluated posts based on their language features and delivered support aligned with the interpreted need. Secondly, it appears that matched support is more likely to occur for requests for informational, relative to emotional support. This finding is promising because some prior work has shown that members of online health community were most dissatisfied when they requested informational support and received emotional support. Matches to solicitations for emotional support had less influence on satisfaction (Vlahovic et al., 2014). This finding also aligns with prior work on the matching of informational and emotional support among Reddit users discussing sexual abuse (Andalibi et al., 2016). Specifically, in their study Andalibi et al. (2016) found that requests for informational support were matched with receipt of informational support, whereas the correlation between emotional support requests and receipt was low. While direct informational support seeking was relatively rare, the fact that community members responded in accordance with the poster’s expressed needs is promising and provides some evidence that users are receiving useful knowledge from peers who may share similar struggles. Prior work has shown that advice giving is common in online communities for self-injury even when it is unsolicited (Smithson et 97 al., 2011), however the present findings seem to suggest just the opposite. TalkLife users are discerning in their exchange of informational support. The non-specific exchange of emotional support, however, should be investigated in further research. Prior work has shown that receiving emotional support when it is not requested, particularly when in need of problem solving or coping advice, can be detrimental and that continued exchange of emotions can lead to overreliance and dependency on online communities (Vlahovic, Wang, Kraut, & Levine, 2014). Phase 3: Factors That Influence Peer Responsiveness In the final phase of this study, I explore factors which influence peer response on TalkLife (RQ 3: Which individual characteristics, message characteristics, and platform features drive peer responsiveness on TalkLife?). Three continuous outcomes variables are examined: (1) comment volume, (2) reaction volume, and (3) response time. Comment volume refers to the number of comments a given post received. Reaction volume refers to the number of one-click reactions a given post received. And, finally, response time refers to the time between when a post was published and when it received the first comment. On average, posts received 3.29 (SD = 5.18) comments and 2.67 (SD = 5.42) reactions. Average response time (for comments only) was 45 minutes (SD = 262, range = 0 – 3080 minutes or 2.1 days). In this analysis the dataset I build upon the coding applied in phase 1 to investigate other factors that may influence the receipt of peer support. I consider how factors at three levels – the individual, the message, and the platform – predict the aforementioned outcomes based on past work and theory described in the literature review. These factors are described below and then I present the analysis and results. 98 Individual Predictors User engagement. User engagement has been operationalized in terms of user activity (e.g., total number of posts, comments, and reactions) as well as the size of user networks (e.g. number of users followed and following). Due to strong correlations among these variables, I employed a composite variable for engagement called user profile. This variable was derived from a latent profile analysis including all 5 factors previously mentioned. Seventy users were in the low, 188 users were in the moderate, and 293 users were in the high engagement profiles. For further details on the derivation of these profiles please see Chapter 2. Gender. Participation in online communities, particularly disclosure practices, can differ by gender. Therefore, gender as a covariate in all models. On TalkLife users self-report their gender when they create a user profile. The data was comprised of 175 males, 362 females, and 41 unidentified users. User tenure. The amount of time users are members of online communities has been linked to activity level and the support they receive (Carron-Arthur et al., 2016; Nguyen & Rose, 2011). User tenure was computed as the difference between two dates: (1) the date a user joined TalkLife and (2) the date data collection closed. On average users were on TalkLife for 280 days (SD = 353). Message Predictors Message type. Several codes applied to posts in phase 1 are employed here including direct (N = 109) / indirect (N = 469) solicitations and mentions of urges (N = 53) and relapse (N = 15). These three variables are binary (i.e., these codes are present or absent). 99 Language use. Linguistic Inquiry and Word Count (LIWC; Pennebaker, Boyd, Jordan, & Blackburn, 2015), a language analysis program, was employed to assess dimensions related to (a) self-relevancy (e.g., first-person pronouns) (M = 13.17, SD = 8.03), (b) emotional valence (e.g., negative emotion [M = 5.90, SD = 7.01], positive emotion [M = 2.95, SD = 4.74]), and (c) indicators of severity (e.g., tentative language [M = 2.74, SD = 4.49], mentions of death [M = 2.29, SD = 5.45], biological processes [M = 4.07 SD = 6.14]). LIWC returns word counts related to these dimensions and they are treated as continuous variables. Platform Predictors When an individual publishes a post on TalkLife, they are prompted to make a number of decisions about information accompanying their post. These decisions have the potential to influence the way in which other users (the audience) perceive and interpret the post, as well as the amount of feedback that the given post receives. These factors are referred to as “platform-level factors” and they include: Category. Post categories are used to classify the content of the post and function much like hashtags do on other social media sites in that they filter content so users can view posts relevant to their own concerns. A binary variable was created to indicate whether the post is associated with the self harm category or another category. Two-hundred sixty-one out of 578 posts fell into the self harm category. Anonymity. Users can also choose whether their post is associated with their username or is displayed anonymously. One hundred fourteen posts in the sample were anonymous. This variable is also binary (e.g., either post was anonymous or identified). 100 Mood checklist. Users must also indicate their mood at the time of posting by selecting among a number of moods on a checklist before their post is published. This information accompanies the post on the main feed. The moods in this mood checklist were further categorized based on valence. Mood valence was treated as a factor with three levels and positive valence as the reference level (e.g., 1 = positive, 2 = negative, 3 = ambiguous). There were 42 posts with positive moods, 410 posts with negative moods, and 123 with ambiguous moods. Data Analysis Plan Diagnostics. Before proceeding with analysis, diagnostics were run to identify potential multicollinearity of variables. Correlations between all variables ranged from < .001 to .28, except for user tenure and engagement (r = .43). I used the mctest package in R to examine variation inflation factors (VIF). The highest variance inflation factor was 1.25 (user tenure), so all variables for analysis could be used without concern for multicollinearity. Model selection. I had planned to run a multi-level analysis with a random effect of user, however preliminary diagnostics revealed that relatively few users had more than one post in the sample (22 users). Of these users, only three had more than 2 posts. Therefore, while the data was nested (posts nested in individuals) it was not appropriate to proceed with an additional intercept in the models. As is often the case with naturally occurring Internet data, all three response variables were highly skewed suggesting over-dispersion. To confirm the best fitting model, I first ran fully saturated quasi-Poisson regressions for each dependent variable (all predictors included). In quasi-Poisson models the dispersion measure (relationship 101 between variance and mean) is freely estimated, rather than constrained to 0 as is the case with Poisson regression. The dispersion measure is a proxy for how many times larger the variance is than the mean. All three models had dispersion measures greater than 1 (comment volume: 6.44, reaction volume: 5.28, response time: 745). Negative binomial regression models are capable of handling over-dispersion and are often employed with count data. To test whether negative binomial models would fit the data, I conducted likelihood ratio test (LRT) comparing the log likelihood of traditional Poisson models with that of negative binomial models, again, including all predictor variables. Significant LRTs confirmed that negative binomial models were more appropriate than Poisson models for the three response variables ps < .000. Thus, I proceeded with analysis using negative binomial regression. Variable selection. Variable selection included two stages. At the first stage, I tested for significance of grouped variables by comparing their fit to the fit of a baseline (intercept only) model. I opted for a grouped stepwise procedure, rather than adding individual variables, because I was interested in which predictor groups had the most influence on peer response. As such some non-significant variables are included in the presentation of the final models. There were four groups: one for individual factors (sex, gender, user tenure, and profile), two groups for message factors (message type from human codes and language features), and one group for platform factors (category, mood, and anonymity). Groups that improved prediction over baseline, through significant p-values and reduction in deviance, were considered for inclusion in the final model. Deviance is a measure of lack of model fit – lower deviance signals a better fitting model. 102 After establishing which predictor groups improved over the baseline model, I proceeded to build final models. To do so, I performed stepwise regression by adding grouped variables cumulatively starting with the group with the most significant p- value from the previously described LRTs. To determine whether the additional group improved prediction I ran likelihood ratio tests on the model with the additional group compared to models at one step prior. For example, at step one I compared the baseline model (intercept) with a model with individual predictors (intercept + sex + tenure + engagement profile). I calculated the deviance of this model and computed a chi square LRT. If the chi square LRT was significant and the deviance was reduced, or stayed approximately the same, I concluded that the additional predictors were an improvement. Results Comment volume. Likelihood ratio tests found that individual factors χ2(4, N = 578) = 27.72 p < .000, message type χ2(3, N = 578) = 9.37, p = .02. and platform factors χ2(7, N = 578) = 15.69, p = .03, each significantly improved prediction of comment volume. Language features, however, did not improve prediction of comment volume χ2(6, N = 578) = 6.688, p = .35. Next, I added predictor groups to the model in ascending order based on their significance from the LRTs above. At each step I again computed LRTs to determine goodness of fit and re-examined deviance measures. A model with platform factors marked significant improvement over the intercept only model (as described above) and reduction in deviance (627.34 – 624.37 = 2.97). Individual factors also increased prediction of comment volume χ2(4, N = 578) = 25.36 p < .000, and reduced deviance (624.37 – 623.72 = 0.65). Finally, 103 message type improved model performance χ2(3, N = 578) = 12.07, p = .007, and deviance was reduced slightly (623.72 – 623.69 = 0.03). Incidence ratios, confidence intervals, and z statistics are included in Table 14. Table 14 Model Predicting Comment Volume IRR Conf. Int. z 2.5, 97.5% Intercept 2.813*** 1.737, 4.644 4.074 Individual characteristics Sex 0.949 0.793, 1.138 -0.562 Tenure/Duration 0.984** 0.9754 -3.265 0.993 Engagement profile …Low to medium 1.401† 0.984, 0.192 1.890 engagement …Low to high 2.215*** 1.552, 3.137 4.450 engagement Message type Direct 1.432** 1.116, 1.854 2.774 Urges 1.410† 1.007, 2.010 1.961 Relapse 0.920 0.501, 1.799 -0.258 Platform factors Anonymity 0.905 0.704, 1.171 -0.776 Category 0.947 0.769 1.166 -0.529 Mood – negative 0.731 0.493, 1.059 -1.616 Mood – ambig. 0.694† 0.453, 1.046 -1.721 Notes. Deviance: 623.69 on 563 df Log Likelihood -1315.38 *** p < .000, ** p < .01, * p < .05, † p < .10 For mood the refence category was positive emotion The results of this analysis suggest that user tenure, engagement profile, and solicitation strategy (direct vs. indirect) contribute to the prediction of comment volume. Specifically, posts from users who have been on TalkLife for a longer duration have an incidence rate of .98. For every unit increase in tenure (month) posts 104 from users receive 2% fewer comments. Posts from moderately engaged users have an incidence rate of 1.40 times that of posts from less engaged users. Posts from these users receive roughly 40% more comments than those from less engaged users. Similarly, posts from highly engaged users have an incidence rate of 2.22 times that of posts from less engaged users. Posts from these users receive more than double the amount of comments as posts from the least engaged users. Direct posts receive about 43% more comments than indirect posts. Finally, there are non-significant trends suggesting the influence of urges and mood on comment volume. Posts mentioning urges receive about 41% more comments than posts not mentioning urges, and ambiguously-valenced posts receive fewer comments (30%) than posts with positive moods. Reaction count. The same procedure described above was employed for predicting reaction count. Likelihood ratio tests found that individual factors χ2(4, N = 578) = 11.24, p = .023, message type χ2(3, N = 578) = 6.56, p = .087, and platform factors χ2(7, N = 578) = 52.59, p < .000, each significantly improved prediction of reaction volume. Again, language features did not improve prediction of reaction volume χ2(6, N = 578) = 8.47, p = .20. Adding individual, message type, and platform factors in a forward stepwise fashion evinced significant improvement in predicting the number of reactions a post received. A model with factors related to message type marked significant improvement over the intercept only model (as described above) and a small reduction in deviance (628.06 – 627.73 = 0.33). When adding individual factors to the message type data prediction of reaction count also improved χ2(4, N = 578) = 11.057, p = .03, 105 and there was a small change in the deviance score (627.73 -627.72 = .01). Finally, inclusion of platform factors also improved the model, χ2(7, N = 578) = 50.70, p < .000, however, deviance slightly increased (627.72 - 629.07 = 1.35). Due to the high significance of this category overall, and relatively small increase in deviance, I opted to include platform factors in the model. Incidence ratios, confidence intervals, and z statistics are included in Table 15. Table 15 Model Predicting Reaction Volume IRR Conf. Int. z 2.5, 97.5% Intercept 6.297*** 2.966, 7.950 10.156 Individual characteristics Sex 1.201* 0.999, 1.446 2.090 Tenure/Duration 0.992† 0.982, 1.001 -2.733 Engagement profile …Low to medium 0.901 0.648, 1.246 -0.629 engagement …Low to high 1.084 0.776, 1.504 0.485 engagement Message type Direct 0.744* 0.577, 0.964 -2.260 Urges 1.316† 0.959, 1.830 1.659 Relapse 1.209 0.686, 2.239 0.636 Platform factors Anonymity 0.905 0.905, 0.709 -0.798 Category 1.025 0.839, 1.253 9.250 Mood – negative 0.352*** 0.246, 0.456 -5.932 Mood – ambig. 0.354*** 0.239, 0.516 -5.333 Notes. Deviance: 629.07 on 563 df Log Likelihood -1207.576 *** p < .000, ** p < .01, * p < .05, † p < .10 For mood the refence category was positive emotion The results of this analysis suggest that solicitation strategy (direct vs. indirect) and mood valence predict reaction volume at p < .05 and p < .00, respectively. 106 Specifically, direct posts had an incidence rate of .74 times indirect post, these posts received about 25% fewer reactions. Posts with negative moods have an incidence rate of .35 times that of posts with positive moods. Similarly, ambiguous moods had an incidence rate of .35 times that of positive moods. In sum, both post with negative or ambiguous moods received about 65% fewer reactions than posts with positive moods. Response time. The analysis for response time is based on a reduced sample of 288 posts because 290 posts in the original sample did not receive any comments. Likelihood ratio tests found that individual factors χ2(4, N = 288) = 73.85 p < .000, message type χ2(3, N = 288) = 12.75, p = .005, language variables χ2(6, N = 288) = 46.19, p < .000, and platform factors χ2(7, N = 288) = 65.10 p < .000 all significantly improved prediction of response time. When adding these four categories to the baseline model in a stepwise fashion I found that individual factors showed significant improvement over baseline (see above). Language features also improved prediction of response time when compared to the individual factors alone, χ2(6, N = 288) = 29.59 p < .000, (deviance: 357.47 – 352.89 = 4.58). Factors related to message type showed significant improvement and reduction in deviance χ2(3, N = 288) = 18.97 p < .000 (352.89 – 349.89 = 3). Finally, platform factors improved prediction of response time χ2(7, N = 288) = 26.16, p < .000, (349.89 -345.80 = 4.09). See Table 15 for statistics on the final model predicting response time. Table 16 Model Predicting Response Time IRR Conf. Int. z 2.5, 97.5% Intercept 610.767*** 2.966, 10.156 8.70 107 Individual characteristics Sex 0.689† 0.446, 1.066 -1.67 Tenure/Duration 0.974* 0.949, 0.998 -2.05 Engagement profile …Low to medium 0.203*** 0.097, 0.427 -4.20 engagement …Low to high 1.125 0.534, 2.367 0.31 engagement Message Type Direct 0.903 0.505, 1.613 -0.34 Urges 4.680*** 2.092, 10.449 3.76 Relapse 0.284* 0.101, 0.079 -2.39 Language Negative emotion 0.995 0.956, 1.035 -0.25 Positive emotion 1.021 0.953, 1.093 0.58 “I” 0.929*** 0.895, 0.963 -4.01 Biological processes 1.000 0.958, 1.046 0.05 Death 1.072* 1.007, 1.141 2.20 tentativeness 0.966 0.913, 1.022 -1.21 Platform factors Anonymity 0.715 0.358, 1.431 -0.95 Category 0.489** 0.309, 0.773 -3.06 Mood – negative 0.287* 0.088, 0.935 -2.07 Mood – ambig. 0.163** 0.052, 0.509 -3.12 Notes. Deviance: 345.80 Log likelihood -1007.29 *** p < .000, ** p < .01, * p < .05, † p < .10 For mood the reference category was positive emotion The results suggest that engagement, mentions of urges, self-relevant language, category and ambiguous moods all predict response time at ps < .01; tenure, relapse, death words, and negative mood predict response time at ps < .05. Specifically, posts from moderately engaged users had an incidence rate of .203 times that of less engaged users. This means that these posts had an 80% quicker response time. Individuals on the site for a longer period of time usually have a quicker response time, IRR of .974. For every increase in year, response time is quicker by 3%. There was no significant difference between low and high 108 engagement, however. Posts that mentioned urges had an incidence rate of 4.68 times that of posts without a mention of urges and posts mentioning relapse had an IRR of .284 times that of posts not mentioning relapse. This means that posts mentioning urges had a longer response time than those not mentioning urges. By contrast, posts mentioning relapse had a 71% quicker response time than posts not mentioning relapse. Posts with self-relevant “I” language have an incidence rate .929 times. Specifically, the percent change in incidence rate of response time is a 3 % decrease for every unit increase in self-relevant language. Posts with death-related language had an incidence rate 1.072 times. That is, the percent change in incidence rate of response time is a .07 % decrease for every unit increase in death words. In other words, it takes longer for people to respond to these posts. Finally, posts in the self-harm category had an IRR of .489 times that of posts in other categories. This suggests that they have almost a 51% quicker response time. Both ambiguous moods (IRR = 0.163, 84%) and negative moods (IRR = 0.287, 71%) have a quicker response time than positive moods. In sum, quicker response times are associated with medium engagement, relapse, self-relevant language, posts in the self- harm category, and negative and ambiguous moods. Discussion The aforementioned analyses provide an understanding of how individuals respond to posts on TalkLife and which predictor groups contribute most to the prediction of peer responsiveness. As a group, language factors contributed least to the overall prediction of comment and reaction volume. However, language was useful in 109 predicting response time. I summarize key findings related to individual, message, and platform factors below and discuss implications. Table 17 provides a summary of the directionality of relationships between all predictors and dependent variables. Table 17 Direction of Relationships for All Predictors and Response Variables Response Comments Reaction time* Individual characteristics Sex Tenure/Duration ¯ ¯ ¯ Engagement profile …Low to medium engagement ­ ¯ …Low to high engagement ­ Message Type Direct ­ ¯ Urges ­ ­ ­ Relapse ¯ Language Negative emotion Positive emotion “I” ¯ Biological processes Death ­ tentativeness Platform factors Anonymity Category ¯ Mood – negative ¯ ¯ Mood – ambig. ¯ ¯ ¯ Notes. ­ = a positive relationship; ¯ = a negative realtionship * for response time a ­ indicates a longer response time whereas a ¯ indicates a shorter response time ¯­ = p < .10 Individual Factors The findings suggest the importance of several individual factors when predicting peer responsiveness. User tenure was inversely related to the volume of 110 support posts received (comments and reactions). For every additional month users are on TalkLife, their posts receive fewer comments and reactions. There are several potential explanations for this finding. First, it may be that members of the TalkLife community allocating resources (in the form of comments and reactions) to newer members. Prior work has shown that members of online communities often respond to new users to make them feel welcome and to teach them the norms of the site early on (Vayreda & Antaki, 2009). Disclosing struggles about self-injury can be difficult and doing so in an online community can mark a significant point in one’s path towards mental health (Naslund et al., 2016). The fact that posts from newer users are receiving attention is promising as it may encourage further beneficial disclosures. Another possibility is that long-time members of TalkLife may be in less immediate need of support. Past work has shown that members of online communities often transition from primarily receiving to primarily providing support (Whitlock et al., 2006; Smithson et al., 2011). This transition from receipt to provision can mark meaningful progress in mental health and well-being. Future work should explore potential differences in the needs of cohorts of individuals perhaps through surveys or interviews. User engagement was also strongly related to the number of comments, but not to the amount of reactions, posts received. Moderate and highly engaged users received significantly more comments on their posts than less engaged users. Additionally, posts from moderately engaged users received quicker responses than posts from users in the low engagement profile. These findings align with prior research on online social capital and reciprocity (Pan, Shen & Feng, 2017). Pan et al. 111 (2017) write that social support can be conceived of as “a substantiation [and] an outcome of social capital” (p. 46); therefore, the more members posters have engaged with directly, and the more visible these posters are in terms of generating content, the more likely their posts are to receive responses from the community. This finding suggests that members who invest time on TalkLife, receive feedback—and past work has shown that this type of active engagement is linked to decreases in distress over time (Barak & Dolev-Cohen, 2006). Practically, this finding suggests that platforms like TalkLife may want to encourage users to generate content and engage in reciprocity on the site. As in recommendations from past work, it may be worthwhile to design features that facilitate development of social capital both because it is related to the amount of support users are likely to receive, and because it is associated with well-being (Ellison & Vitak, 2015; Burke, Kraut, & Marlow, 2011; Burke, Marlow, Lento, 2010). Finally, it appears that females and unidentified users receive more comments on posts, and a quicker response, than males. Past work has shown that males engage in lower rates of reciprocity (Wang et al., 2016) however, males may also face increased stigma for certain disorders like self-injury which have been traditionally, but incorrectly, associated with females (Law, Rostill-Brookes, & Goodman, 2009). Therefore, platforms like TalkLife may wish to incentivize acknowledgment of users who are least likely to engage in help-seeking (like males). Designing for a more even distribution of support could be an avenue for future work. Message Characteristics Solicitation strategy was an important predictor of both comment and reaction 112 volume. While indirect posts were more common, I found that direct posts received more comments and fewer reactions. This finding is congruent with past work (Shah, 2017Liu et al., 2017; Bambina, 2007; Burke, Joyce, Kim, Anand, & Kraut, 2007). Solicitation strategy did not impact the amount of time before posts received a first response, however. It is important to note that indirect posts are not always enacted for interpersonal purposes. In fact, in line with the functional model of disclosure (Bazarova & Choi, 2014), the results of the content analysis in part 1, suggest that some indirect posts may have been enacted to fulfill an intrapersonal need for keeping personal record or catharsis through disclosure. While the findings do not shed light on the relative benefits of these two types of indirect posts (intrinsic and extrinsic motivations) this is a potentially important and interesting line for future work. Posts mentioning urges and relapse, the two self-injury relevant themes from the content analysis, had different impacts on peer responsiveness. Posts mentioning urges received more comments and reactions than posts without such mentions. This finding is consistent with Liu et al.’s (2017) findings on disclosures of “recovery problems” in an online forum for alcohol use disorder and past work on social media use and self-injury (Brown et al., 2018). For example, more explicit or direct references to self-injury in posts on Instagram (e.g., more severe depictions of wounds) was positively associated with volume of comments received (Brown et al., 2018). While it is promising that posts with urges receive support, it is also possible that these comments serve a reinforcing function. Prior research suggests that individuals can build dependencies on the feedback that they receive in online forums and that this can thwart recovery process or inhibit help-seeking (Whitlock et al., 113 2006). More work is needed to understand the effects of receiving online support while experiencing an urge—and whether this support diminishes or reinforces the experience. Response time was longer for posts mentioning urges (M = 97.1, SD = 404.60) relative to those that do not mention urges (M = 39.37, SD = 240.60). This finding is in contrast to the triage model of support and perhaps suggests a misallocation of resources (at least in terms of timely allocation). Users of online forums for self-injury turn to their communities for crisis support when they experience an acute urge (Rodham et al., 2007). However, prior work has shown that users can be triggered by other people discussing their struggles—particularly when they are graphic depictions (Lewis & Baker, 2011; Lewis, Heath, Sornberger, et al., 2012; Whitlock et al., 2006). TalkLife allows members to set up trigger warnings and 14 of the 53 posts with mentions of urges included trigger warnings for their content. Posts with these trigger warnings received a quicker average response (M = 2.89, SD = 3.76) than posts without (M = 135, SD = 478). As is clear in the presentation of means and standard deviations, there is greater variation in response time for posts without trigger warnings. Slower responses on posts with mentions of urges, and specifically those with no trigger warning, may reflect individuals engaging in self-protective strategies. That is, members may be hesitant to engage with these types of posts—which are typically characterized by high arousal emotions—for fear that it will induce a difficult emotional state in themselves and escalate the situation for themselves or the original poster. Future work should further probe into this difference in response volume and response time for posts mentioning urges, with and without trigger 114 warnings. Using a more refined coding system to assess the influence of urge severity may be a useful step forward. In contrast to urges, relapse was a key predictor of response time, but not response volume. Posts mentioning relapse received a quicker response than posts without mentions of relapse. Since the overall portion of posts coded as relapse is quite small these results should be interpreted cautiously. Unlike posts mentioning urges, posts indicating relapse were usually characterized by low arousal feelings such as hopelessness, disappointment, and shame. It may be that TalkLife users feel more capable of consoling or providing encouragement to individuals who have relapsed, than they do de-escalating intense emotions associated with urges. Past work has shown that when deciding whether to respond to distressed posts, individuals evaluate the likelihood of their feedback being useful (Chang et al., 2018). Again, this finding merits attention in future work. Finally, while language features have proven to be valuable in detecting mental health patterns, including supportive exchanges online, I did not find much evidence for this in this work. Other message characteristics, features of the platform, and individual variables outperformed language features in predicting both comment and reaction volume. However, self-relevant language and death words were predictive response time. Posts with self-relevant language received a quicker response time, whereas posts with death words received a slower response time. Past work has shown that self-relevant and death-related language are associated with suicide risk (O’Dea et al., 2017; Cheng, Li, Kwok, Zhu, & Yip, 2017). Here we find that these types of words are responded to differently. Again, it would be important to understand the 115 context in which death language is mentioned. I note a big contrast in action or highly aroused posts mentioning death (e.g., suicidal posts) versus posts passively mentioning wanting to die. It is important for future work to explore this relationship and for designers to implement automatic features providing at-risk individuals with timely support. Platform Features Of the platform features, mood valence proved to be the strongest predictor, influencing both comment and reaction volume. Specifically, when users selected ambiguous moods from the mood checklist (M = 3.34, SD = 5.45) their posts received fewer comments than posts with positive moods (M = 4.90, SD = 8.99) and posts with negative (M = 2.35, SD = 2.61) and ambiguous moods (M = 2.40, SD = 2.62) received fewer reactions than posts with positive moods (M = 6.71, SD = 17.51). However, ambiguous (M = 32.35, SD = 147.35) and negative moods (M = 34.35, SD = 200.83) were indicative of a quicker response time, relative to positive moods (M = 195.19, SD = 684.89). This finding is somewhat in line with the triage model of support—in that, negative and ambiguous moods receive quick feedback, but the fact that these types of posts receive less support is troubling. This may be indicative of the normality of sharing negative moods and general hopelessness on the site. Further, users may feel that they do not have much more to offer posters with negative moods, than what was offered in the initial response. Finally, post category was also associated with response time, but not response volume. Specifically, posts in the self-harm category received a quicker response (M = 36.71, SD = 263.96) than posts in other categories (M = 54.44, SD = 262.27). This 116 finding is in line with the triage model of support and may indicate that TalkLife users are particularly attuned to this category or that they feel a sense of responsibility to respond to posts explicitly linked to self-harm. Conclusion In this work I investigated the prevalence of peer support, the relationship between solicitation styles and the type of support received, and factors associated with peer responsiveness among individuals who self-injure on TalkLife. In so doing this work makes a number of key contributions to the literature. First, while studies of online behaviors among individuals who self-injure explore a number of different sites including Tumblr (Seko & Lewis, 2016), Instagram (Brown et al., 2017), YouTube (Lewis & Knoll, 2015), and self-injury forums (Whitlock et al., 2006), there is limited prior work exploring the exchange of peer support on TalkLife (with the exception of Pruksachatkun, Pendse, & Sharma, 2019) or similar mobile peer support applications. Given that the structure and use of online platforms can differ substantially, it is critical to examine how the peer support dynamics play out in different contexts. Secondly, while many of the findings from the content analysis regarding topics discussed on TalkLife align with past work on online communities, the aforementioned analyses are the first to investigate the relationship between solicitation and response, and factors contributing to support, in this depth, and quantitatively, in an online community of individuals struggling with self-injury. Finally, the results of this work generate a number of important directions for future work. These areas include: 117 (1) Exploring the motivations behind direct and indirect strategies by interviewing users of the TalkLife platform. Given that direct posts receive more feedback, in general, researchers or designers might explore ways to encourage direct disclosures for more tailored feedback. (2) Investigating slow response to messages with urges and death-related content. Some relevant questions pertaining to this finding are: How are TalkLife users making decisions about how to respond to these posts? To what extend are members engaging in self-protective strategies? (3) Exploring whether the types of support received are perceived as useful. Qualitative methods such as interviews or a more thorough quantitative content analysis coding for topical matches between solicitation and response could be a useful direction forward. (4) Investigating the effect of receiving various peer support after disclosing urges. Little is known about how online support communities impact urges over time. Longitudinal studies, like the one presented in Chapter 4, could explore this further. Limitations While I believe this work makes important contributions, it is not without limitations. First, while I sampled for individuals who had at one time posted to the self-harm category, or had been flagged by a self-harm classifier, it is possible that I captured some individuals not struggling with self-injury. Secondly, the coding scheme identified many dimensions of peer support but could be improved upon. Prior work suggests that the direct/indirect dichotomy is not sufficient what examining 118 support solicitation and I found this to be a limitation of the current study as well. Future work should refine the coding scheme to capture dimensions of direct and indirect strategies, as well as broaden dimensions of informational and informational support to include sub-categories including the exchange of specific types of experiential knowledge. Finally, several of the codes (relapse and urges) were infrequent and this may lead to an over or underrepresentation of results in the present analyses. I am currently working on developing machine learning classifiers to identify these codes in the larger dataset. 119 CHAPTER 4 EXAMINING THE RELATIONSHIP BETWEEN TALKLIFE USE AND SELF- INJURY OUTCOMES Self-injury is a common and concerning behavior that can signal significant psychological distress and deficits in emotional or interpersonal coping (Evans, Hawton, Rodham, 2005; Klonsky, 2009). Reported to affect 13% of American adolescents (Swannell, Martin, Page, Hasking, & St. John, 2014), self-injury is an independent risk factor for future suicidal behaviors (Klonksy, May & Glenn, 2013; Whitlock, Eckenroe, Purington, Baral Abrams, Barreira, & Kress, 2013). For a variety of reasons, including stigma and a lack of readiness to change, many individuals who self-injure do not disclose their behavior to anyone (Evans, Hawton, & Rodham, 2005, Nixon, Cloutier, & Jansson, 2008; Whitlock, Muehlenkamp, Purington, Eckenrode, Barreira, Baral Abrams, et al., 2011). While low disclosure rates can impede upon the potential for intervention, many individuals discuss their experiences with self-injury rather openly in online forums (Coulson, Bullock, & Rodham, 2017; Lewis & Seko, 2016; Whitlock et al., 2006). Researchers have examined self-injury discourse online and theorized on the potential benefits and risks of participation in such forums (Lewis & Seko, 2016; Rodham, Gavin, Lewis, St. Denis, & Bandalli, 2013; Whitlock et al., 2006). Prior work has provided a rich understanding of topics discussed in online communities (Lewis & Knoll, 2015; Rodham, Gavin, & Miles, 2007) and individual motives for participation (Lewis & Michal, 2016; Lewis, Rosenrot, & Messner, 2012; Rodham et al., 2007; Whitlock et al., 2006). However, the bulk of this literature has been 120 descriptive, cross-sectional, and focused on relatively small samples. Implicit in this past work is the assumption that online communities can impact self-injury behaviors and thoughts in some meaningful way. Yet, to date, there are few empirical studies examining the effects of participation in online communities on self- injury outcomes and practices (Valencia-Agudo, Burcher, Ezpeleta, & Kramer, 2018; Lewis, Seko, & Joshi, 2018; Dyson, Hartling, Shulhan, Chisholm, Milne, Sundar et al. 2016). To increase our understanding of how the risks and benefits previously identified translate to behavioral outcomes we must diversify our methodological approach and use multiple data formats (e.g., log data, survey data, etc.). Recent advances in computational techniques have enabled researchers to track patterns of online behaviors to predict mental health status and future risk (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016; Pavalanathan & De Choudhury, 2015). In these studies, online communities become contexts to explore the lived experience of sensitive, or stigmatized, mental illnesses. Specific behavioral and linguistic patterns on social media sites have been used to predict when individuals are experiencing post-partum symptoms (De Choudhury, Counts, Horvitz, & Hoff, 2014), depression (De Choudhury, Gamon, Counts, & Horvitz, 2013), and to identify individuals who are at risk of future suicides (e.g., De Choudhury et al., 2016; Jashinsky, Burton, Hanson, West, Giraud-Carrier, Barnes & Argle, 2014). However, such methods have not yet been robustly applied to understand contexts which contribute to and predict self-injury behaviors. Repeated survey methods, such as those from diary studies and ecological momentary assessments (EMA), are a second method that has value when aiming to 121 understand contexts associated with mental health. EMA studies on self-injury have identified antecedents and consequences of self-injurious thoughts and behaviors and empirically validated the functions the behavior serves (Rodríguez-Blanco, Carballo, & Baca-García, 2018; Kranzler, Fehling, Lindqvist, Brillante, Yuan, Gao et al., 2018; Armey, Growther, & Miller, 2011; Andrewes, Hulbert, Cotton, Betts, & Chanen, 2017). The present work employs a mixed methods approach to address the gap in knowledge on the relationship between participation in online forums for self-injury and self-injury behaviors, thoughts, intentions to injure, and ability to resist urges over time. This study combines computational and survey methods to investigate how the emotional, cognitive, and social contexts manifested through behavior and language in an online peer forum, are associated self-injury outcomes. This work provides empirical support for the relationship between participation in online forums and self- injury outcomes and begins to articulate mechanisms contributing to this relationship. Literature Review The present study is informed by several lines of past work, including theories and empirical work on the functions of self-injury, descriptive studies articulating the benefits and risks of participation in online communities, and recent work on prediction and contexts related to self-injury behaviors and thoughts. Functions of Self-injury Past work suggests that self-injury behaviors can serve at least two functions:(1) intrapersonal functions, or efforts to manage or change one’s internal state and (2) interpersonal functions, aimed at influencing one’s external environment 122 (Klonsky et al., 2015; Klonsky, 2007; Klonsky & Glenn, 2008). Emotion regulation— generating or diminishing undesirable affective states—is the most commonly cited intrapersonal function of the behavior (Klonsky, 2007). Converging evidence from ecological momentary assessments (EMA) and laboratory studies shows that self- injury behaviors result in changes in affect (e.g., Armey, Crowther, Miller, 2011; Klonsky, 2007; Russ, Roth, Lerman, Kakuma, Harrison, Shindledecker et al., 1992; Haines, Williams, Brain, & Wilson, 1995). Non-suicidal self-injury (NSSI) is also associated with impaired interpersonal functioning, poor quality relationships, and low perceived support (Turner, Wakefield, Gratz, & Chapman, 2017; Baetens, Claes, Muehlenkamp, Grietens, & Onghena, 2012). Interpersonal theories of self-injury suggest that relational experiences can be antecedents and consequences of the behavior (Prinstein, Guerry, Browne, & Rancourt, 2009). Self-injury may be enacted to signal distress, elicit social support, escape undesired interpersonal situations, or to solicit belonging/acceptance within a group (Turner et al., 2017). While intrapersonal functions appear to be more common than interpersonal functions (Taylor, Jomar, Dhingra, Forrester, Shahmalak, & Dickson, 2018), some work suggests that relational functions are not as easily recognized by individuals in self-report measures and that they may be particularly important in the initiation, maintenance, and cessation of NSSI for adolescents (Rodríguez-Blanco et al., 2018). Indeed, peer influence is one proposed mechanism for NSSI initiation. Prinstein and colleagues (2010) found that adolescents who have friends who engage in NSSI are at greater risk for future NSSI. Further, Giletta and colleagues (2013) uncovered an 123 indirect influence of interpersonal relationships such that adolescents who had friends with characteristics related to self-injury (depression, impulsivity) were at greater risk for future NSSI. Given that online communities are often frequented by young people and that, by nature, these spaces are interpersonal, relational factors (e.g., volume of support exchanged, affiliation with the group) merit further attention as they could provide insights into participatory risks (e.g., normalization and over-reliance on the group) (Lewis & Michal, 2014; Whitlock et al., 2006). A need for future work examining interpersonal contexts influencing self-injury, including digital contexts, has been acknowledged (Valencia-Agudo et al., 2018; Dyson et al., 2016). Potential Risks and Benefits of Online Forums A growing body of literature exists on the potential benefits of online peer-to- peer support networks for individuals with mental health concerns (Kazdin, 2017; Naslund, Aschbrenner, Marsch, & Bartels, 2016; Melling & Houguet-Pincham, 2013). While some studies find that online peer support communities can be beneficial, other work substantiates a growing concern over the potential for adverse effects. In the context of self-injury, the most commonly referenced benefits to participation are accrued through the exchange of social support, increased sense of belonging and validation, and through access to other resources (Lewis & Michal, 2014; Lewis & Arbhuthnott, 2014). For example, a recent survey assessing the therapeutic affordances of a self-harm online support community found evidence for social connection (e.g., mutual support) and exploration (e.g., learn strategies, seek information) (Coulson, Bullock, & Rodham, 2017). Online forums also allow 124 individuals to narrate their experiences and self-reflect (e.g., share experiences, personal clarity) (Coulson, Bullock, Rodham, 2017). Further, some research suggests that online forums can be useful to assuage urges because forum use can serve as a distraction (Baker & Fortune, 2008) and members can find “just in time” support (Rodham et al. 2007). By contrast, risks associated with participating in online forums include potential reinforcement of the behavior, excessive focus on emotional suffering and rumination, and exposure to triggering content (Rodham, Gavin, Miles, 2007; Whitlock et al., 2006, Smithson et al., 2011). While finding and connecting with others is frequently reported as a facilitator of recovery, participation in online communities can result in normalization of self-injury behaviors and, for some, can lead to an over-reliance on the community for support (Whitlock et al., 2006). Exchanges in online communities can downplay the serious consequences of the behavior (Rodham et al., 2007) and discourage professional help, either explicitly or implicitly through sharing past negative experiences (Whitlock et al. 2006). Social identity perspectives on recovery or behavior change emphasize the critical role of community identification in supporting the change process (Miller, Wakefiled, & Sani, 2018; Turner et al., 1987). Community norms which include seeking help and practicing behavioral cessation can have a positive impact on individuals in recovery; however, when the only point of connection in a community is the behavior itself this can be problematic as it makes the behavioral identity salient (Schofield & Eurich-Fulcer, 2004). Indeed, prior work notes that some forum participants feel the need to maintain an “injury identity” (Adams et al., 2005) where 125 enactment of NSSI is seen as critical to community membership (Adler & Adler, 2008). In sum, when behavior is seen as normative, community members can be motivated to perform the behavior to maintain group status. This idea that individuals must engage in the behavior in order to validate the severity of their experiences has been found in other studies and is seen as a key risk of community involvement (Baker & Lewis, 2013; Sternudd, 2012). Exposure to triggering content is another risk associated with online participation. In studies of self-injury communities some members report that graphic content curbs urges to injure and can dissuade them from future NSSI acts because it presents them with severe cases (Sternudd, 2012; Baker & Lewis, 2013; Rodham et al., 2013), whereas others describe seeking out content in online forums to self-trigger self-injury urges (Lewis & Michal, 2014; Harris & Roberts, 2013; Murray & Fox, 2006). Evidence for the relationship between self-injury behavior and online community use is similarly mixed. Murray and Fox (2006) found that just over 40% of 79 respondents reported that participation in an online discussion group reduced their self-injurious behavior—whereas 11% reported that it initiated behavior. In a survey study of a clinical population of 12 – 17-year-olds, greater exposure to NSSI content on social media was associated with NSSI engagement (Zhu, Westers, Horton, King, Diederich, Stewart et al., 2016). However, this study may have limited generalizability to non-clinical populations. Finally, a recent systematic review of literature on social networking and self-harm concludes that greater time spent on online social networking promotes self-harm behavior and suicidal ideation in vulnerable 126 adolescents (Memon, Sharma, Mohite, & Jain, 2018). In particular, internet addiction (Pan & Yeh, 2018; Lam, Peng, Mai & Jing, 2009) and cyberbullying (Hay & Meldrum, 2010) have been associated with self-harm. Lam and colleagues (2009) found that individuals who were moderately or severely addicted to the internet were twice as likely to report self-injurious behaviors (Lam, Peng, Mai, & Jing, 2009). Thus, research points to the significant potential to participation in online communities to positively and negatively influence self-injury. Research examining this relationship and teasing out the mechanisms contributing to certain outcomes is needed. Prediction: What Is Likely to Influence Outcomes? Two additional lines of work can be informative when thinking about characteristics and contexts associated with self-injury outcomes: (1) diary and EMA studies and (2) computational mental health research. Diary and ecological momentary assessment. Diary and EMA methods have enabled researchers to gather real-time data on behaviors and states related to mental health. A recent meta-analysis of all EMA studies on NSSI identified emotional, cognitive, and social contexts in which NSSI occurs, motives that lead to NSSI, and mechanisms that may influence or predict NSSI (Rodríguez-Blanco et al., 2018). Congruent with the emphasis on intrapersonal functions in the larger literature, the majority of these studies focused on emotional precursors and consequences of self- injury (Armey et al., 2011; Andrewes et al., 2017; Kranzler et al., 2018). Emotion. Several EMA studies find that self-injurious behaviors are flanked by changes in affect which can be apparent up to 15 hours prior to NSSI acts (Armey 127 et al, 2011; Andrewes et al., 2017). The most consistent finding in the literature is that negative affect often precedes SI behaviors (Armey et al., 2011; Muehlenkamp, Engel, Wadeson, Crosby, Wonderlich, Simonich et al., 2009; Hughes, King, Kranzler, Fehling, Miller, Lindqvist, & Selby, 2019). There is also some evidence for decreases in negative affect (Kranzler et al., 2018) and increases in positive affect following the behavior (Muehlenkamp et al., 2009). For example, in a 7-day study conducted by Armey et al., (2011), researchers found increases in negative affect prior to NSSI acts, which subsequently faded away gradually with enactment of self-injury (Armey, Crowther, & Miller, 2011). Muehlenkamp et al. (2009) similarly found increases in negative affect and decreases in positive affect prior to a self-injury event. Other work suggests that comparing positive and negative affect is not sufficient; to understand emotional contexts surrounding self-injury we need to attend to specific emotions. In a recent 2-week long EMA study, Kranzler et al., (2018) found a decrease in high-arousal negative emotions, and an increase in low-arousal positive emotions, following self-injury behaviors. Specifically, participants reported decreases in sadness, anger, hurt/rejection, frustration, anger, loneliness and overwhelm but no change in feelings of being ashamed, embarrassed, or guilty following self-injury. Additionally, participants reported increases in positive emotions such as being calm, content, proud, happy but not excitement or feelings of rush. There is also limited evidence for different emotional contexts leading to NSSI thoughts and behaviors. For example, in several studies NSSI thoughts were elicited in relation to sadness and anxiety (Nock et al., 2009, Shingleton et al., 2013), whereas 128 NSSI behavior was associated with rejection and anger (Nock et al., 2009; Turner, Yiu, Clas, Muehlenkamp, Chapman, 2016). While most studies have focused on discrete emotional experiences, recent studies have acknowledged the importance of emotional lability (Santangelo et al., 2017; Anestis et al., 2012). Emotional lability refers to the tendency to experience emotions in a dynamic manner with extreme shifts in emotion lasting up to a few days (Trull et al., 2008). For example, Santangelo and colleagues (2017) found that compared to individuals with no NSSI history, individuals who self-injure experience more affective instability when assessed through longitudinal diary assessments. Further, Anetis and colleagues (2012) found that frequent shifts in emotional intensity and valence were associated with more NSSI episodes. Importantly, both studies recognize the importance of comorbidity in the generalizability of their work–BPD in the prior and bulimia nervosa in the latter study. Cognitive states. In addition to emotional states, certain cognitive patterns have been associated with self-injury (Cha, Wilson, Tezanos, DiVasto & Tolchin, 2018). Several studies have shown the relationship between NSSI and ruminative thinking (Hoff & Muehlenkamp, 2009; Selby, Connell, & Joiner, 2010). Rumination is common in other mental illnesses such as depression however, like emotional lability, fluctuations in intensity of rumination (known as instability of rumination) are theorized to play a key role in NSSI. Indeed, Selby and colleagues (2013) found that rumination instability (fluctuations in rumination) and fluctuations in negative affect interacted to predicted daily reports of NSSI in a 2-week study. This works supports the emotional cascade model of NSSI—which suggests that NSSI functions as a 129 distraction from cascades of negative affect and emotion. Similarly, Hughes et al. (2019) found that repetitive negative thinking and negative affect (particularly anxiety and feelings of overwhelm) predicted NSSI thoughts and behaviors. Again, the findings reported in this section support the need to examine specific emotions. For Selby et al. (2013), the strongest effects of rumination instability were observed when individuals reported sadness. For Hughes et al. (2019), repetitive negative thinking amplified the effects of anxiety and overwhelm. Social contexts. Finally, given the importance of interpersonal relationships in NSSI, several studies have investigated social contexts giving rise to the behavior. In a study focused on disordered eating and NSSI, Turner and colleagues found that interpersonal contexts were more likely to give rise to NSSI, whereas intrapersonal contexts were associated with disordered eating patterns (Turner et al., 2016). Specifically, participants were more likely to act on thoughts of NSSI following arguments and feelings of rejection. Similarly, in another study assessing urges, mood, conflict and perceived support, Turner, Cobb, Gratz, and Chapman (2016) found that interpersonal conflict was related to greater same-day NSSI urges and acts. The act of revealing NSSI to others was associated with greater perceived social support, however, this perceived support also increased the likelihood of an individual engaging in NSSI the following day. Individuals who disclosed their NSSI to others during the study reported more perceived support and more total NSSI acts. This work provides support for interpersonal reinforcement of NSSI behaviors (Nock & Prinstein, 2004; Klonsky, 2007). 130 In sum, the aforementioned research highlights contexts predictive of NSSI behaviors and confirms past work on the intrapersonal and interpersonal functions of NSSI. While EMA studies improve upon cross-sectional work by accessing data “in real time,” these methods often rely on self-report of both predictor and response variables. However, people are not always consciously aware of social and emotional states. In a sample of young people diagnosed with borderline personality disorder, 1/3 were unable to identify motives for behavior when asked and nearly 1/2 unable to remember what was happening prior to self-injury (Andrewes et al., 2017). Another method that has proven to be useful in understanding the emotional, cognitive and social contexts around events, without relying exclusively on self-report, is computational analyses of media log data. Past work suggests that this naturally occurring data can shed light on one’s mental health status (De Choudhury et al., 2014). Further, online data can contain information pertinent to individual experiences with stigmatized conditions, including contexts which bring individuals to the community and needs which are, and are not, being fulfilled within the community (Lewis & Michal, 2016; Rodham et al., 2013). Computational mental health research. Prior work has shown that behavioral patterns and linguistic features of posts in online communities can distinguish between people with and without a number of conditions and predict mental health risk (De Choudhury et al., 2014; 2013). Much of this work has combined behavioral measures such as the frequency with which people post in online communities, with linguistic features such as language use or themes embedded in published content. 131 For example, in a number of studies on depression, De Choudhury and colleagues identified factors associated with the illness through language use. Individuals with depression showed greater negative emotion, higher self-attentional focus, increased relational and medicinal concerns, and heightened expression of religious thoughts, relative to those without known depression (De Choudhury et al., 2013). Behaviorally, individuals with depression showed less engagement, social activity, and reduced reciprocity (De Choudhury et al., 2013). Similarly, suicidal ideation has also been associated with self-attentional focus, reduced social engagement, and expressions of hopelessness, anxiety, impulsiveness, and loneliness (De Choudhury et al., 2016). Computational methods have already been extended to other health domains such as to understand abstinence and relapse for behaviors such as smoking (Murnane & Counts, 2014) and alcohol addiction (Bliuc, Doan, & Best, 2018). While this type of approach shows great promise in understanding mechanisms underlying experience with mental health, and can guide needed interventions, self- injury has not yet been thoroughly explored despite self-injury language being included in analyses of other mental health concerns, as a symptom or indicator of severity (Andalibi, Ozturk, & Forte, 2017; Chancellor, Lin, Goodman, Zerwas, & De Choudhury, 2016). The present study seeks to explore self-injury behavior more directly through computational methods. Based on the foregoing review I have identified several gaps in literature. First, prior research on self-injury through EMA and diary studies have been limited in the populations of study and generally focus on intrapersonal, rather than interpersonal, 132 functions. Only 5 of the 23 studies in Rodríguez-Blanco and colleague’s 2018 systematic review included adolescents despite this being a population commonly affected by NSSI (Rodríguez-Blanco et al., 2018). The authors note that “more studies on adolescents with NSSI behavior are badly needed. Given the importance of interpersonal and familiar relations in adolescence, the social function of NSSI might be more relevant in this young population than in adulthood” (Rodríguez-Blanco et al., 2018, p. 217). Secondly, in light of functional models of NSSI, and research on risks associated with online community involvement, more research is needed on the interpersonal dynamics associated with NSSI behavior and thoughts. Individuals frequently turn to online venues for information and support around the behavior so interpersonal functions, while perhaps less commonly endorsed, may be more likely to exert influence over behaviors online. Finally, there are no studies connecting online behaviors to self-injury behaviors or thoughts – despite overwhelming evidence for the presence of self-injury discussions on the Internet and inclinations that online community use can impact behaviors. The present study seeks to address these limitations by exploring how self- injury behaviors and thoughts are related to activity and language use on TalkLife, an online forum which is primarily comprised of adolescents and young adults with various mental health concerns. Self-injury outcomes are modelled (e.g., behavior, thoughts, intentions, ability to resist) as a function of online engagement and language manifested in content. I focus on both intrapersonal and interpersonal factors related to self-injury in prior work and target mechanisms related to potential risks and benefits of participation. The rich, naturally occurring data assessed in this study can advance 133 our understanding of the mechanisms leading to self-injury behaviors and thoughts and may be useful for risk assessment, intervention, and prevention. Given the novelty of this work and scant prior work to draw on in online contexts I proceed in an exploratory manner and ask a number of research questions. Specifically, I ask two high-level research questions with sub-parts to probe potential mechanisms identified in the prior literature review. I ask: RQ1: What behavioral patterns are associated with self-injury acts, urges, thoughts, ability to resist? RQ2: What language patterns are associated with self-injury acts, urges, thoughts, ability to resist? To understand the first research question, I assess the potential for a dose- response relationship—wherein activity level predicts self-injury outcomes. RQ1a: What is the relationship between activity level on TalkLife in the preceding week and self-injury outcomes in the subsequent reporting period (e.g., SI behavior, frequency, thoughts, ability to resist, and intentions)? I also pose two sub-questions regarding the impact of triggering content on self-injury outcomes. Prior work shows that some individuals use online communities to trigger themselves (Lewis & Michal, 2014; Harris & Roberts, 2013; Murray & Fox, 2006), while others find exposure to self-injury content diminishes urges and intentions to injure (Sternudd, 2012; Baker & Lewis, 2013; Rodham et al., 2013). To probe into this relationship between triggering content and self-injury, I examine how viewing triggering content (dismissing trigger warnings) is related to self-injury outcomes (including intentions to injure and ability to resist urges to injure). I 134 additionally examine the relationship between the number of posts users publish with trigger warnings and self-injury outcomes. RQ1b: What is the relationship between dismissing trigger warnings on TalkLife in the preceding week and self-injury behavior, frequency, thoughts, ability to resist, and intentions? RQ1c: What is the relationship between publishing posts with trigger warnings on TalkLife in the preceding week and self-injury behavior, frequency, thoughts, ability to resist, and intention (urges)? The second research question examines language predictive of self-injury outcomes. Based on interpersonal models of self-injury (Klonsky, 2009; Prinstein, Guerry, Browne, & Rancourt, 2009; Klonsky, 2009), I study the association between community involvement and themes of family and friends and self-injury outcomes. Past work suggests that one risk of participating in online forums is over-identification with the self-injury identity. As a proxy for identification with the community I assess affiliative language as in past work (Biluc, Doan, & Best, 2018). RQ2a: What is the relationship between affiliative language in the preceding week and self-injury outcomes? RQ2b: What is the relationship between mentions of family and friends in the preceding week and self-injury outcomes? Finally, I examine the relationship between emotional states and self-injury. RQ2c: What is the relationship between specific emotional states (e.g., positive and negative emotions, rumination, lability) in the preceding week and self-injury outcomes? 135 Data and Methods I employed a mixed methods approach with surveys and naturally-occurring log data over a four-month period to understand how use of the online peer support forum, TalkLife, is related to self-injury outcomes, with the ultimate goal of identifying predictive patterns. Three types of data are included in analyses: (1) responses to surveys, (2) behaviors on the platform (e.g., TalkLife activity), and (3) language use in posts and comments. A description of data acquisition, relevant measures, and treatment of these measures in my analyses follows. Survey Data Surveys were issued on a rolling basis over a period of 12 weeks. The first and last surveys were administered on October 25, 2018 and January 17, 2019. Survey administration was triggered internally by a TalkLife classifier identifying suspected self-injury content. Once a participant’s post was flagged, they received a prompt to answer surveys once a week for the duration of the study period. Due to this method the total duration of the study for any given participant varied (e.g., if a participant received the first survey on the 11th week of data collection they only had the opportunity to receive 2 surveys total). Additionally, participants could opt out of weekly surveys at any point. Given my interest in patterns over time, I constrained the final dataset to participants who had completed at least 2 surveys and extracted corresponding behavioral/language data based on this criterion. Participants who failed to complete basic demographics or who did not complete at least one self-injury outcome variable in a survey were eliminated from the sample. The number of surveys participants 136 completed varied (M = 2, SD = 1.20, range: 1 – 10 surveys), as did the time between surveys (M = 1.74 weeks, SD = 2.15 weeks, range: 1 – 11.6 weeks). The total number of participants included for final analyses was 268 with 697 survey observations. Measures for analysis. Surveys included 9 items that were administered weekly with a question each addressing (a) presence of self-injury behaviors (“In the past week, did you purposely hurt yourself without wanting to die?”), (b) frequency of behaviors (Over the past week, how many times have you purposely hurt yourself without wanting to die?), (c) thoughts (In the past week, did you have thoughts of purposely hurting yourself without wanting to die?), (d) intentions (Overall, in the past week, how strong has your intention to purposely hurt yourself without wanting to die been?), and (e) ability to resist (Overall, in the past week, how strong has your ability to resist the urge to purposely hurt yourself without wanting to die been?). Additionally, there were items for past experience with therapy, age of first self-injury, and demographics (e.g., age, race, gender) (For full survey please see Appendix G). Self-injury items (a – e above) were treated as dependent variables and demographics were included as covariates in all models. Response categories for self- injury behavior and thoughts were “Yes,” “No,” and “I don’t know.” For simplicity I re-coded these as binary responses with 1 indicating “Yes.” Ability to resist urges and intentions to injure were both answered on a 5-point scale ranging from 0 (not at all strong) to 4 (very strong). These variables were treated as continuous. Finally, frequency of self-injury behaviors required participants to enter a number. Four participants reported engaging in self-injury at highly improbable rates (> 500 times). To correct for these outliers, and not lose these participants, I capped reduced these 137 values to 100. Thus, the final self-injury frequency variable ranged from 0 to 100. Behavioral Data De-identified behavioral data for participants meeting the above criterion (2 or more surveys) were sourced with license and consent from the TalkLife platform. This data included metadata as well as original posts and comments. Given that weekly surveys referred to self-injury activity in the week prior, I used behavioral data one week before each weekly survey as the primary data for prediction. Due to the rolling basis of survey administration, I controlled for differences in time (relative to survey number) in all analyses. Measures for analysis. I focus on several measures in analyses, including: (1) activity level (average of posts, gifts, reactions, comments, likes, and users followed in week prior), (2) the number of posts a user published with trigger warnings, and (3) the number of times a user dismissed trigger warnings when looking at others’ posts. All of these variables averaged at the day level. As previously mentioned, variance in behavior (also referred to as lability or instability) has proven to be a meaningful independent predictor of mental health in past work (De Choudhury, Counts, Horvitz, & Hoff, 2014). I include two measures to capture fluctuations in activity: variance and rate of change. Variance was computed at the day-level for all behavioral measures (e.g., activity, trigger posts, trigger dismiss) in the week prior to a given survey. This measure was computed as 𝜎 $, where the mean activity level in a given week (µ) was subtracted from the activity on a given day (c) for all days of the week, this was summed (S), squared, and divided by 7 (N). 138 Σ(𝜒 − 𝜇)^2 𝜎 $ = Ν This variance measure provides a sense of how an individual’s log data (e.g., activity, publishing and viewing triggering content) is distributed over the week. A high variance score suggests more lability or more change in activity over the course of the week (e.g., users are very active one day, have no activity the next, and then are moderately active). Next, I computed a change score to capture the magnitude of change between proximal behavior (behavior in the week prior to a survey) and more distal behavior (the remaining time between surveys). This measure was adapted from prior work is sometimes called “rate of change” (De Choudhury, Counts, Horvitz, & Hoff, 2014). To account for differences in the amount of time in the remaining period I, again, averaged to the day-level. That is, change (∆) was equal to the mean activity in a week prior to a survey (A) - the mean activity in the remaining period (B). A positive change score indicates that there was more activity on a daily basis in the week prior to a survey, whereas a negative difference indicates more activity in the distal period. ∆ = 𝐴 − 𝐵 This change variable was entered into models as a continuous, including negative and positive values. Thus, interpretation of this variable should be as a “rate of change” – or the magnitude of difference between the two time periods. A large value indicates a large difference between activity in the week before a survey, relative to time before, whereas a small value signals relatively similar activity in these two time periods. 139 I opted to compute this change score rather than simply assessing time between surveys, as a whole, to maintain granularity in analysis. The survey questions refer to the week prior, and most empirical studies of self-injury have observed shifts in behavior and cognitions within day or week time frames. Thus, this additional change score allows me to focus on proximal activity, while also investigating the predictive power of the difference in proximal and more distal periods. Due to their high positive skew, all behavioral measures were log-transformed to restore normality. Final variables for the behavioral data include: (1) activity, (2) trigger posts, (3) trigger dismiss, (5) variance (x 3), and (6) change between proximal and distal activity (x 3). Language Data All posts and comments made by participants within the study period were pre- processed according to Mell and Gill’s (2010) recommendations and run through the Linguistic Inquiry and Word Count program, a psycholinguistic text analysis tool that is frequently employed in research on mental health (Pennebaker, Boyd, Jordan, & Blackburn, 2015). Measures for analysis. Relevant dimensions were identified from the preceding literature review, including: (1) affect (e.g., positive emotion and specific negative emotions [sadness and anger]), (2) social/relational (e.g., mentions of family or friends), (3) affiliative language (e.g., affiliation and we pronouns based on Tausczik & Pennebaker, 2010; Bliuc Doan, Best, 2018), (4) self-focus (e.g., “I” language), (4) rumination (a composite score of negative emotion and focus on the past), and (5) efficacy (a composite score of focus present, future, and certain 140 language as in Bliuc et al., 2018). As with the behavioral data, I computed lability scores (variability and change) for select language dimensions based on prior research. Based on the Emotional Cascade Model (Selby et al., 2013) and empirical findings on role of instability of rumination and negative affect prior to NSSI episodes (Hoff & Muehlenkamp, 2009; Selby, Connell, & Joiner, 2010), I include variance and change for rumination, positive emotion, sadness, and anger. Data Pre-processing All variables described above were tested for multicollinearity with the R package mctest. Separate analyses were run for each dependent variable. The highest variance inflation factor (VIF) factor was consistently reported for rumination (8.92 – 9.58) followed by “I” (4.22 - 5.18). Multicollinearity was not detected so I proceeded with analyses without excluding any variables at the outset. Sample Characteristics Participants. Descriptive statistics for survey data are provided in Table 18. The sample was comprised of 268 participants who were mostly female (55.6%), White (61.2%), and were around the age of 19. Over 40% of participants reported having received therapy at some point during the study period and the median age of first self-injury was 14. On average, participants were registered on TalkLife for about 10 months (SD = 12.8; Mdn = 5.49, .46 – 109 months) and had posted a median number of 49 posts (M = 147.66, SD= 309.72), and 360 comments (M = 1775.28, SD = 4092.56). Based on findings from chapter 2, these descriptives suggest that these participants were 141 more highly engaged than average TalkLife users. During the 4 months of this study, 130 participants reported injuring and 227 reported having self-injury thoughts. Of those who reported injuring, the median weekly frequency was 3 times. Two-hundred thirteen participants reported having thoughts of self-injury without engaging in self-injury behavior. By contrast, only 7 participants reported self-injury behaviors without also reporting self-injury thoughts. Table 18 Participant Characteristics Demographics (N = 268) Age 20.4 (4.34) (Mdn= 19) (range: 13 – 38) Gender Male 83 (31%) Female 149 (55.6%) Transgender/non-binary 36 (13.4%) Race White 164 (61.2%) Black 13 (4.9%) Asian 43 (16%) Other 47 (17.5%) History Average age at onset 15 years old (Mdn = 14) (range: 5 – 37) Therapy ever 113 (42.2%) Weekly self-injury items Total participants who injured 130 (48.5%) frequency of behavior 7.86 (16.8) (Mdn = 3) (week) Total participants with SI 227 (84.7%) thoughts Average SI resistance score* 2.12 (1.45) (Mdn = 2 “somewhat strong”) Average SI intention score* 2.07(1.51) (Mdn = 2 “somewhat strong”) Notes. * these were measured on a 5-point scale ranging from 0 (not at all strong) to 4 (very strong). Data Analysis Plan 142 I examined the relationship between TalkLife activity and self-injury outcomes using multilevel analysis to account for the nested structure of this data. Survey responses, TalkLife log data, and language data, were nested in the level of the participant—therefore, I included a random effect of participant in all analyses. Five models were run predicting (1) self-injury behavior, (2) self-injury thoughts, (3) ability to resist urge to injure, (4) intentions to injure, and (5) behavioral frequency. All data were analyzed in R. Logistic regressions predicting behavior and thoughts were analyzed using the lme4 package and linear models predicting ability to resist, intentions, and frequency were analyzed with the nlme package. Variable selection. I began with full models including all 31 variables described above (4 control variables, 3 log variables, 10 language variables, 8 variance measures and 8 change scores). These full models were subsequently reduced via backwards variable selection with two stopping points: (1) a relaxed constraint model including all variables that were significant at p < .10 and (2) a more parsimonious model where all variables were significant at p < .05. A complete set of models can be viewed in Appendices H – L; however, I report on the most parsimonious models in the present paper. For the binary dependent variables (behaviors and thoughts) logged coefficients were exponentiated for easier interpretation. Results To facilitate comparisons across related self-injury dimensions I report beta coefficients, standard errors, and significance levels for all variables in Tables 19 and 20. Odds ratios are also reported for logistic regressions in Table 19. Results for each dependent variable are discussed separately, and in greater detail, in text. 143 Self-Injury Behavior Several demographic factors were predictive of self-injury behavior including age and race. Specifically, the odds of engaging in self-injury behavior decreased linearly with age. When compared to the youngest group (17 and under), odds of self- injury decreased by a factor of 0.24 (CI: 0.08 – 0.69, p = .008) for individuals between 18 and 24 years of age, and by a factor of .09 (CI: 0.02 – 0.39, p = .001) for individuals 25 and older. In other words, the youngest cohort was about 75% and 90% more likely to report behaviors than both older cohorts, respectively. Relative to White participants, those who identified as American Indian or Alaskan Native, Native Hawaiian or Pacific Islander, and Other had lower odds of engaging in self-injury behavior (OR = 0.23, CI: 0.07 – 0.73, p = .01). While gender did not emerge as a significant predictor in this model, a marginal trend suggests that females were at higher odds of engaging self-injury behavior, relative to males (OR = 2.53, CI: 0.98 – 6.53, p = .05). In terms of TalkLife log data, the odds of engaging in self-injury behavior increased with the amount of posts published with trigger warnings in the week prior. For every additional post published with a trigger warning in the week prior, the odds of engaging in self-injury behavior increased nearly 5-fold (OR = 5.37, CI: 1.25 – 23.05, p = .02). Additionally, as the rate of change increased, the odds of self-injury behavior decreased (OR = 0.81, CI: 0.68 – 0.98, p = .03). In other words, for every one-unit increase in change between the number of times individuals dismissed trigger warnings in the week of a survey, relative to time before, the likelihood of self-injury behavior decreased by roughly 20%—I return to this finding in the discussion. 144 Table 19 Predictors of Self-Injury Outcomes Behavior Thoughts Resist Intention (n = 265, obs = 632) (n = 266, obs = 636) (n = 267, obs = (n = 267, obs = 605) 613) B SE OR B SE OR B SE B SE Intercept -0.07 0.64 0.93 1.78** 0.67 5.91 2.10*** 0.24 2.43*** 0.26 Covariates Age (13 – 17) (18 – 24) -1.44** 0.54 0.24 -0.42 0.48 0.66 0.25 0.19 -0.05 0.20 (25 – 38) -2.42** 0.75 0.09 -1.18† 0.63 0.31 0.15 0.25 -0.58* 0.26 Gender (Male) Female 0.93† 0.48 2.53 0.72† 0.42 2.05 -0.24 0.17 0.25 0.18 Trans / other 0.76 0.68 2.13 0.28 0.62 1.32 -0.50* 0.24 0.13 0.26 Race (White) Black -0.25 0.95 0.78 -0.83 0.82 0.44 0.03 0.35 0.03 0.37 Asian -0.68 0.61 0.51 -1.38* 0.55 0.25 -0.54* 0.21 -0.51* 0.23 Other -1.46* 0.58 0.23 -0.61 0.49 0.55 -0.17 0.20 -0.20 0.21 Time -0.22 0.13 0.80 -0.03 0.12 0.97 0.06 0.05 0.01 0.04 Log Data Activity -0.45* 0.18 0.64 -0.37*** 0.09 Trigger posts 1.68* 0.74 5.37 2.88* 1.22 17.87 Trigger dis 1.39* 0.66 1.50* 0.61 Language I -0.07* 0.03 Family -0.63** 0.22 Efficacy 0.14* 0.06 Change Trigger posts -1.22* 0.55 0.29 Trigger dis -0.20* 0.10 0.81 -0.06* 0.03 Positive emo 0.05* 0.02 Variance Trigger dis -0.18* 0.08 Anger 0.19* 0.09 Rumination 0.14* 0.06 1.15 Deviance 674.6 667.2 2075.7 2037.3 Res. Variance – – 1.32 0.978 Notes. *** p < .000, ** p < .01, * p < .05, † p < .10 Self-Injury Thoughts Race was a significant predictor of self-injury thoughts. Asian participants were at lower odds of reporting self-injury thoughts (OR = 0.25, CI: 0.09 – 0.73, p = 145 .01), when compared to White participants. Marginal effects emerged for both age and gender. Specifically, females had higher odds of reporting SI thoughts (OR = 2.05, CI: 0.91 – 4.65, p = .08), relative to males; whereas participants between the ages of 25 – 38 were less likely to report thoughts than participants between 13 – 15 (OR = 0.31, CI: 0.09 – 1.06, p = .06). For log data, activity level emerged as a significant predictor of self-injury thoughts. Greater active use of TalkLife (as indicated by posts, comments, likes, etc.) was associated with lower odds of reporting self-injury thoughts (OR = 0.64, CI: 0.45 – 0.90, p = .01). For every one-unit increase in activity, the odds of self-injury decreased by 36%. By contrast, the amount of trigger posts published was positively related to self-injury thoughts—for every additional post with triggering content the odds of self-injury behavior increased by a factor of 17.87—nearly 17-fold (CI: 1.644, 194.150, p = .02). The rate of change of posting triggering content also predicted self-injury thoughts. As this change decreased, the odds of having self-injury thoughts increased (OR: .29, CI: 0.10 – 0.86, p = .02). Finally, greater variation in ruminative language was associated with greater odds of self-injury thoughts (OR = 1.15, CI: 1.02 – 1.29, p = .02). Ability to Resist Self-Injury Gender and race emerged as significant predictors of participants’ ability to resist self-injury. Transgender and non-binary participants reported less ability to resist self-injury, relative to male participants (b = -0.50, SE = .24, p = .04). Similarly, Asian participants reported less ability to resist, relative to White participants (b = - 146 0.54, SE = .21, p = .01). For log data, the amount of trigger warnings dismissed was positively related to ability to resist (b = 1.39, SE = .66, p = .03). That is, for every additional trigger post viewed, ability to resist self-injury also increased. Several language dimensions were significant predictors of ability to resist. Specifically, the use of self-referent language (“I”) was negatively associated with ability to resist (b = -0.07, SE = 0.03, p = .01) whereas the use of “efficacy” language was positively associated with ability to resist injuring (b = 0.14, SE = 0.06, p = .01). As the change score for positive language increased, ability to resist also increased (b = .05, SE = .02, p = .04). This means that the greater the magnitude of difference between positive language used in the week before a survey, and time before that, the more participants reported being able to resist urges. The variance in dismissing trigger warnings was negatively associated with ability to resist such that lower variance (or more stability) was associated with greater ability to resist (b = - 0.18, SE = 0.08, p = .02). By contrast, greater variance of anger expressions was associated with greater ability to resist urges to self-injure (b =.19, SE = .09, p = .03). Intentions to Self-Injure Age and race were predictive of intentions to self-injure. Participants in the oldest age group were least likely to report intentions to injure (b = -0.58, SE = .26, p = .02) and Asian participants were less likely to report strong intentions to injure (b = -0.51, SE = 0.23, p = .02), relative to White participants. For log data, I note a negative association between activity and intentions to injure (b = -0.37, SE = .09, p < .001). By contrast, there was a positive association 147 between the amount of trigger warnings dismissed and intentions to injure (b = 1.50, SE = .061, p = .01). I also found that mentions of family were negatively associated with intentions to injure (b = -0.32, SE = 0.22, p = .01). Finally, I note a negative relationship between the change in dismissing trigger warnings and intentions to injure such that greater changes in viewing triggering content were related to less intention to injure (b = -0.06, SE = .03, p = .04). Self-Injury Frequency As a final analysis I ran a multilevel linear regression model predicting self- injury frequency. The response variable was log transformed to adjust for its significant skew; however, the data were not over-dispersed. I found that age was predictive of frequency. When compared to the youngest group (17 and under), individuals between 18 and 24 (b = -0.26, SE = 0.12, p = .03), and individuals between 25 and 38 (b = -0.44, SE = 0.16, p = .006) reported less frequent self-injury behaviors. Frequency of self-injury behavior was slightly higher in females, than in males—though this failed to reach conventional levels of significance (b = 0.19, SE = 0.11, p = .08). Race did not emerge as a significant predictor of self-injury frequency. Regarding log data, there was a positive relationship between the number of posts published with triggering content in the week prior and the frequency of self- injury behaviors reported in the following report period (b = 0.45, SE = 0.15, p = .002). I also note a positive relationship between “I” language and frequency of self- injury behaviors (b = 0.02, SE = 0.01, p = .04). Table 20 Predictors of Self-Injury Frequency (n = 267, 673 observations) B SE 148 Intercept 0.59*** 0.15 Covariates Age (13 – 17) (18 – 24) -0.26* 0.12 (25 – 38) -0.44* 0.16 Gender (Male) Female 0.19† 0.11 Trans / other 0.21 0.16 Race (White) Black -0.02 0.22 Asian -0.15 0.14 Other -0.19 0.13 Time -0.01 0.02 Log Data Trigger posts 0.45** 0.15 Language I 0.02* 0.01 Deviance 1549.7 Res. Variance 0.35 Notes. *** p < .000, ** p < .01, * p < .05, † p < .10 Summary In general, in response to the first set of research questions I found relationships between (1) TalkLife activity level and self-injury behavior and intentions, (2) publishing triggering content and self-injury behavior and thoughts, and (3) viewing triggering content and ability to resist and intentions to injure. In response to the second set of research questions, I found (1) no relationship between affiliative language and self-injury outcomes, (2) that mentions of family were related to intentions to injure and (3) lability of positive emotions and anger was related to ability to resist and ruminative lability was related to self-injury thoughts. I discuss these findings more fully in the discussion. Discussion In this study, I employed survey responses and naturally-occurring log data to predict self-injury outcomes. This study is one of the first to connect patterns in 149 behavioral and language traces to self-reported self-injury outcomes and offers new insights into the relationship between participation in online communities and self- injury. In general, demographic findings largely align with existing literature on self- injury prevalence and risks. Age was predictive of self-injury behaviors and intentions to injure such that the oldest cohort was least likely to report self-injury behaviors or strong intentions to engage in self-injury behaviors. Past work has shown that self- injury often (but not always) follows a developmental trajectory. For example, Nakar et al. (2016) examined self-injury over the course of adolescence and found that behaviors peaked at mid-adolescence and declined towards 18 years of age. Similarly, in a sample of transgender young people, Jackman et al. (2018) found that younger individuals were more likely to report past year self-injury. Age was not predictive of self-injury thoughts or ability to resist urges to injure, however. The absence of this relationship is not entirely surprising given what is known about the durability of thoughts and temptations even after harmful behaviors have ceased. Stage of change models (e.g., the Transtheoretical Model of Behavior Change; Prochaska & Velicer, 1997) show that significant cognitive and behavioral efforts go into changing behaviors and that even when behaviors diminish, individuals can experience thoughts and cravings (such as smoking, alcohol use, over- eating) (Prochaska, Redding, & Evers, 2015). It is theorized that as individuals are more advanced in their change process these thoughts/temptations no longer exert strong influence over behavior, due to the cultivation of new cognitions, self-efficacy, and coping strategies (Prochaska, Redding, & Evers, 2015). Indeed, I note some 150 convergence with this idea in the finding that “efficacy” language was predictive of ability to resist self-injury urges—a finding I return to later in the discussion. Several trends with gender are also noteworthy. Females were slightly more likely to engage in self-injury behaviors and report self-injury thoughts, relative to males (Whitlock et al., 2011; Laye-Gindhu & Schonert-Reichl, 2005). However, these differences did not meet conventional levels of significance in these models. A significant effect of transgender and non-binary participants emerged suggesting that these participants had the lowest ability to resist urges to injure. Gender dysphoria has been identified as an NSSI risk factor (Jackman et al., 2018) and some work suggests that LGBTQ individuals engage in self-injury at higher than average rates (Jackman et al., 2016; Lytle, Blosnich, & Kamen, 2016; Lefevor, Boyd-Rogers, Sprague, & Janis, 2019). Explanations for these high rates often invoke minority stress theory (Hendricks & Testa, 2012) which describes how a number of distal (e.g., discrimination, violence) and proximal stressors (e.g., self-stigma, expectancies) disproportionately faced by gender and sexual-orientation minorities, contribute to health disparities (Hendricks & Testa, 2012; Meyer, 2003). Several studies have shown that these stressors can influence coping strategies and resilience in the face of life challenges (Meyer, 2003; Bockting, Miner, Romine, Hamilton, & Colman, 2013). In the context of the present study, an inability to resist self-injury when tempted, may signal a lack of other protective factors and be a potential target of future intervention. Since a non-trivial percentage of the participants identified as non-binary or transgender, this might suggest that applications, like TalkLife, can be useful targets for additional skill building interventions. Commonly cited methods for resisting urges 151 to injure include keeping busy, being around friends or family, and talking about feelings (Klonsky & Glenn, 2008). It may be possible to leverage these sites for more than these ends, by delivering psychoeducational materials or exercises for developing interpersonal or intrapersonal coping skills. Ethnic minority participants, by contrast, reported lesser odds of engaging in self-injury, and Asian participants, in particular, report lower odds of having thoughts, and less ability to resist and intentions relative to White participants. Several studies of college samples report similar findings regarding the lower prevalence of NSSI in ethnic minorities (Whitlock et al., 2011; Muehlenkamp & Gutierrez, 2007). However, the relationship between certain ethnic groups and self-injury outcomes (e.g., thoughts, behavior, ability to resist, and intentions) is not well-understood and should be explored further. Most studies to date rely on data derived from relatively small sample sizes, and highly specific samples (e.g., college, community, clinical). It may be that certain minority groups are associated with different risks and protective factors and this could have important implications for intervention. Finally, the findings that Asian participants report lower ability to resist self- injury and fewer intentions to injure may appear contradictory. However, it is important to consider the possibility that this finding is an artifact of the question stem. For example, if participants had low intentions to injure, they may also report low ability to resist because there was no need to resist. In fact, a weak negative correlation was observed between these two variables (r(612) = -.13, p < .05). Future longitudinal work should seek clarity on this, perhaps by simply varying question stems. Log Data 152 While the demographic findings add to a growing literature on prevalence rates and risk factors, the most significant contributions of the present study are in the relationships that emerged between TalkLife log data and self-injury outcomes. In general, I found relationships between (1) TalkLife activity level and self-injury behavior and intentions, (2) publishing triggering content and self-injury behavior and thoughts, and (3) viewing triggering content and ability to resist and intentions to injure. Recall that existing literature suggests online activity can be both beneficial and detrimental to individuals who self-injure (Daine et al., 2013; Marchant et al., 2017). Online forum use has been associated with self-reported increases in frequency and severity of wounds (Harris & Roberts, 2013) and exposure to content has been linked to engagement (Zhu et al., 2016). However, other reports suggest that participation can curb self-injury thoughts/behaviors through distraction and increased support and resources (Sternudd, 2012; Baker & Lewis, 2013; Rodham et al., 2013). Findings from the present study most closely align with this latter assertion. Indeed, I found that TalkLife activity was predictive of both decreased thoughts and intentions to injure but was not directly related to self-injury behaviors or ability to resist. Participants who actively engaged on TalkLife – through posting content, liking, and generally interacting with others – were at lower odds of reporting thoughts and intentions. There are several possible explanations for these findings. First, it may be that individuals who are active on TalkLife are simply less plagued by day-to-day thoughts or urges to injure. These individuals may be in more advanced stages of the behavior 153 change process or they may be qualitatively different from other users in some way. However, I feel this explanation is not likely. Past work has shown that individuals who are on online communities are often very early in the stage of change process (Grunberg & Lewis, 2015) and I would expect these individuals to have relatively high reports of thoughts and behaviors. A more promising explanation might be that active use of TalkLife is serving to diminish NSSI thoughts and intentions. This explanation aligns with some prior work which shows that individuals who engage in active use of social media are more likely to derive benefits such as feelings of support, connections with others, and companionship (Burke, Marlow, & Lento, 2010; Ellison, Steinfield, & Lampe, 2007; Verduyn et al., 2017; Frison & Eggermont, 2015). By contrast, posting triggering content was positively associated with self- reported self-injury thoughts and behaviors. In other words, while active use appears to be indicative of fewer thoughts and intentions to injure, the types of content posted – specifically content that has been labeled as triggering—is predictive of increased risk of SI behaviors and thoughts. Given the nature of the present analyses it is not possible to infer the causal direction of this. Triggering posts may have been published before or after self-injury thoughts or behavior. It is possible that the participants posted triggering content after they had injured or in response to self-injury thoughts. In qualitative analyses of this data in chapters 2 and 3, I found that it was common for participants to describe self-injury episodes or relapse and, as part of a site norm, these posts were often flagged as triggering. Thus, it appears that triggering content—and the trigger feature associated with the TalkLife platform––has promise in predicting self-injury, though the directionality of this relationship should be explored in future 154 work. Dismissing trigger warnings, and viewing triggering content, appears to be positively related to both abilities to resist urges to self-injury and intentions to injure. While seemingly contradictory, I feel that these findings might suggest that participants with strong intention to injure, dismiss trigger warnings in order to view triggering content and dissuade them from engaging in the behavior. In so doing, individuals may feel more capable of resisting self-injury. Indeed, this speculation aligns with findings from other work—that seeing or reading graphic content makes them see how bad it can get and thus they feel less urge for injury—but the directionality of this should be explored in future work (Sternudd, 2012; Baker & Lewis, 2013; Rodham et al., 2013). Language Several language dimensions were also useful in predicting self-injury outcomes. For example, the use of self-referent language (“I”) in posts was negatively associated with self-reported ability to resist. In other words, as participants used more “I” language, their ability to resist self-injury decreased. These findings are largely in line with work showing that self-referent language is associated with poor mental health status. In studies of essays and social media posts by depressed, versus non- depressed, individuals, texts written by individuals with current depression reliably have more first person, singular language (De Choudhury et al., 2016, Tackman et al., 2018; Rude, Gortner, & Pennebaker, 2004). By contrast, use of efficacy language (e.g., will, soon, always) was positively associated with ability to resist injury. These findings align with past work on self- 155 efficacy in recovery or abstinence of problematic behaviors (Marlatt, Baer, & Quicley, 1997; Watson et al., 2006; Vauth et al., 2007). It would be important for future work to explore exactly how this efficacy language is being used in context on TalkLife. I used a composite variable for general efficacy in this work, but past work has shown that efficacy is a complex construct with many facets not wholly captured in the present study (Bandura, 1977). How individuals evaluate their capacity to motivate and behavior can also vary significantly as one’s relationship to self-injury changes and is thus a central target of many intervention and therapeutic modalities (Muehlenkamp, 2006). For example, self-efficacy mediates the relationship between stigma and well-being (Vauth et al., 2007) and group identification can increase self- efficacy (Watson et al., 2006). This finding is promising both because it provides further evidence for the role of efficacy in ability to resist self-injury urges and provides some validity for the method of connecting language traces to self-report surveys. As familial language increased intentions to injure decreased. Past work shows that family support is critical to NSSI cessation (Whitlock, Prussein, & Pietrusza, 2015; Tatnell et al., 2014, Brausch & Gutierrez, 2010) and family disharmony is a key NSSI risk factor (Linehan, 1993). In particular, perceived parental support has been found to directly relate to NSSI and poor relationship quality with parents is associated with frequency of NSSI (Di Pierro, Sarno, Perego, Gallucci, & Madeddu, 2012). Thus, the finding that mentions of family decreases intentions to injure is aligned with the literature and converge on the general idea that family can have a protective influence over self-injury. However, I did not explore the contexts in which this language was 156 mentioned in the present study—and this could be a decisive factor. In a study using online community data to predict self-harm risk, for example, Soldaini et al. (2018) found that mentions of family predicted reduced risk and the maintenance of risk status. The authors conducted a qualitative examination of these posts to further probe the relationship and describe that posts mentioning family and conflict, or distress, are associated with risk, whereas mentions of family and home are associated with reduced risk. Thus, an important area for future work will be to explore the contexts in which familial language is used to better determine the mechanisms associated with positive outcomes. Lability Scores Patterns in emotional expressions were also observed for self-injury thoughts and ability to resist. Specifically, greater variance in rumination was predictive of self- injury thoughts. That is, participants were more likely to have thoughts of self-injury when posts in the week prior to a survey varied in use of ruminative language. This is in line with Selby et al. (2013) found that rumination instability (fluctuations in rumination) predicted daily reports of NSSI. I found no evidence for rumination predicting self-injury outcomes, however (Selby et al., 2010; Selby et al., 2013; Buelens et al., 2019). By contrast, higher levels of variance in anger expressions was associated with a greater ability to resist urges to self-injure. This finding is interesting in the absence of a main effect of anger. Many individuals who self-injure have trouble dealing with negative emotions and anger is often cited as an affective state leading to self-injury (Klonsky, 2007). However, I find that fluctuation in anger expressions over the course 157 of a week is related to a capacity to resist urges. It is possible that fluctuations in anger (or an ebb and flow of this affective experience) signal healthier adaptive processing or simply an ability to express anger and a variety of other emotions. Past work has shown that expressing anger can be helpful for chronic pain patients, for example (Graham, Lobel, Glass, & Lokshina, 2008). Variance in dismissing triggers was negatively associated with ability to resist. As variance increased—that is participants may have viewed a lot of triggering content on one day versus no triggering content on another day—ability to resist decreased. By contrast the other proxy for lability, the rate of change between proximal (week prior) and more distal (remaining time between surveys) time periods- - shows a negative relationship between the magnitude of change and risk of self- injury behavior and intentions to injure. As the change score increased, the likelihood of self-injury, and intention to self-injure decreased. Together these findings are perplexing. Variability in the use of the trigger dismiss variable in the short term is linked to poorer ability to resist urges—yet change in this activity from the week before the survey, relative to a time period before that, is related to higher likelihood of self-injury and self-injury intentions. Future work should probe more deeply into this complex relationship. Variance reflects instability so it may be that variance in viewing triggering content is characteristic of maladaptive coping. The change score represents something qualitatively different about the week before a survey and a time period before this week. The findings suggest that when there is something quite different about this week self-injury is more likely and intentions are also higher. Thus, from a prediction perspective high rates of change should signal potential risk. 158 Finally, I found that as the rate of change of positive emotion increased, so did ability to resist self-injury. Expressions of positive emotion have been associated with improved well-being in past work and when looking at a dummy coded version of this rate of change variable I find that higher positive emotion in the week prior—relative to the time before—is associated with greater ability to resist (Tausczik & Pennebaker, 2010). Limitations The findings reported here are preliminary and while I describe them in terms of being predictive, it is important acknowledge certain limitations. First, it is important to note that while I feel that the use of naturally occurring online data can shed light on behaviors – and perhaps improve upon self-report previously used in EMA studies – many aspects of the participants’ experiences are not readily captured in these measures. Second, while the use of LIWC categories for language analysis does not account for context. The finding that family language is linked to lower intentions to self-injury is interesting but there are likely contexts when this language is associated with negative outcomes. Future work may wish to use alternative methods for conducting language analysis such as the tf-idf or word co-occurrence measures derived from n-grams. Third, I operationalized TalkLife activity as “active” use. Due to high correlations between active and passive measures (e.g., viewing behaviors) in this dataset I restricted the analysis to active log data to avoid issues of collinearity. However, research has shown that active and passive social media use can have 159 differential effects on affective well-being (Verduyn et al., 2015; Burke, Marlow, & Lento, 2010). Thus, future work should explore the influence of a more comprehensive spectrum of engagement on self-injury outcomes. A randomized controlled trial of TalkLife, which is currently underway, seeks to address this gap. Fourth, the methodological design imposes limitations on causality. Survey questions were framed to ask about any self-injury in the week prior but did not ask about specific days, or times of day, when these events (e.g., thoughts, behaviors) occurred. Therefore, it was not possible in the present analyses to determine with certainty when the self-injury events occurred relative to the activities on TalkLife. Future work might consider daily surveys, either through diary or ecological momentary assessments, for a more nuanced understanding of temporal relationships. Lastly, selection-bias likely limits the generalizability of these findings. Participants in this study were inclined to use applications for support, were willing to take weekly surveys on their self-injury and were engaged in active use of the application. This suggests that participants may have been similar in ways related to their use and their readiness to change self-injury behaviors. Future work may wish to consider how to recruit, and incentivize, a more diverse population for a broader picture of TalkLife activity. 160 CHAPTER 5 GENERAL DISCUSSION AND IMPLICATIONS In this dissertation I set out to understand how individuals use of a mobile peer support application, TalkLife, to discuss and exchange support on self-injury experiences. I described user characteristics and natural use patterns and then I investigated (a) how individuals approach support-seeking, (b) what types of support they commonly solicit and receive, and (c) how these online activities impact self- injury outcomes. This research contributes the following to the literature: (1) An in-depth description of self-injury-related activity on TalkLife, including user characteristics, natural use patterns, and common language in posts and comments. (2) An empirical examination of peer support on TalkLife including proportions of direct and indirect support seeking and different supportive responses (e.g., emotional, informational, companionship, etc.). (3) Identification of individual, message, and platform factors which predict the amount of support posts receive and the amount of time it takes for community members to respond to certain types of posts. (4) A longitudinal examination of the relationship between naturally occurring log data and several self-injury outcomes including self-injury behaviors, thoughts, urges, and intentions to injure. In this final chapter, I will summarize and synthesize key findings and discuss implications for three groups of stakeholders (developers, clinicians, and researchers). In doing so, promising directions for future research are also identified and discussed. 161 Summary of Key Findings The aim of chapter 1 was to review relevant literature on self-injury and online communities and identify areas in need of further study. The pervasiveness of online activity related to self-injury was well-documented in the extant literature, as were the potential risks and benefits of participating in online communities. However, less was known about the mechanisms of peer support in online communities and no prior research had established a link between naturally occurring online activity and self- injury outcomes. Thus, I aimed to describe a community of self-injuring individuals on the mobile peer support application, TalkLife, and then address the aforementioned gaps in the empirical chapters of this dissertation (chapters 2 – 4). The purpose of chapter 2 was to deeply explore TalkLife users and content at a descriptive level. I set out to answer the following questions: (1) who is using TalkLife to discuss self-injury?, (2) how do individuals use the application?, and (3) what language attributes and behaviors characterize use among individuals who have disclosed self-injury? Through my analyses I found that the demographics of users, in terms of age and gender, were similar to those represented in other online self-injury communities. In all three samples explored in this dissertation, users were mostly female and of adolescent to young adult age. Additionally, users reported being on the site for around 4 months on average, though participants in the sample from Chapter 4 were on the site for longer (10 months). How users engaged on the application varied significantly. I observed three archetypes of TalkLife users based on engagement level (e.g., low, moderate, and highly engaged users). This distribution followed a power law where most users 162 engaged passively and a much smaller proportion were highly active (Rains, 2018; Carron-Arthur et al., 2016; van Mierlo, 2014). Language analyses revealed that it was common to disclose emotional experiences and negative emotions were particularly salient in posts. Users frequently referred to their social life and relationships. Many users appeared to use TalkLife when they were in conflict with friends or family, suggesting that this application may fill a need when users do not feel supported in their offline lives. Users also asked the TalkLife community for tips on how to minimize self-injury behaviors, and the impacts of these behaviors on other aspects of their lives (e.g., school, friends, family). At the time of writing this, no prior work had been published on the self-injury community on TalkLife. Thus, findings of this descriptive chapter were a critical first step towards understanding the function of TalkLife for its’ users. As a whole, this chapter suggests that at least in terms of use patterns and demographics, TalkLife is similar to other online communities in the literature – perhaps affording some generalizability of the implications to other social applications. In chapter 3 I conducted an in-depth investigation of peer support on TalkLife. I focused on defining the prevalence of peer support types and explored the relationship between solicitation styles and the type of support received. Moreover, I identified factors which predicted peer responsiveness in terms of comment and reaction volume as well as quickness of response. I used quantitative content analysis and built predictive models to answer the following questions: (1) What types of peer support exist and are common on TalkLife?, (2) How is the type of support sought related to the type of support provided in comments? And (3) What individual, 163 message, and platform characteristics drive peer responsiveness on TalkLife? In general, I found that indirect posts were far more common than direct posts and among indirect posts, negative disclosures outweighed positive and ambiguous disclosures. For direct posts, requests for emotional support were more common than requests for informational support. Further, direct posts were more likely to receive informational support and indirect posts were more likely to receive emotional support. In sum, the match between informational support appeared to be far stronger than the match between emotional support. In the analyses on peer responsiveness, I found that individual, message, and platform characteristics were most predictive whereas language features added little to prediction of comment or reaction volume but did contribute to the prediction of response time. For specifics on factors that were predictive please refer to chapter 3 or Table 21 (below). In general, direct posts were associated with a greater volume of comments and fewer reactions; negative and ambiguous moods received fewer reactions than positive moods; and highly engaged users received more comments, relative to users who engaged less frequently. Finally, in chapter 4 I conducted a longitudinal analysis of the relationship between self-injury outcomes (e.g., behavior, thoughts, urges, and ability to resist) and behavioral and linguistic patterns. I put forth the following research questions: (1) What behavioral patterns are associated with NSSI behaviors, thoughts, intentions, and ability to resist?, (2) What language patterns are associated with NSSI behaviors, thoughts, intentions, and ability to resist? Additionally, I probed potential mechanisms behind exposure to triggering content, and intrapersonal and interpersonal 164 (social/relational dimensions of language use) predictors of self-injury outcomes. Relationships emerged between (1) TalkLife activity level and self-injury thoughts and intentions, (2) publishing triggering content and self-injury behavior and thoughts, and (3) viewing triggering content and ability to resist urges and intentions to injure. Specifically, greater engagement on TalkLife was associated with decreased thoughts and intentions to injure, whereas posting triggering content was associated with increased likelihood of engaging in behaviors and having self-injury thoughts. By contrast, viewing triggering content was related to a greater ability to resist and a greater intention to injure. I also noted several interesting patterns related to language use and behavioral and affective patterns, including that ruminative lability is associated having self-injury thoughts and changes in posting, and consuming, triggering content predict self-injury behaviors and thoughts, respectively (see Chapter 4 for full details). All results are discussed in detail in their respective chapters. The remainder of this chapter is devoted to outlining implications and discussing recommendations for three key stakeholder groups: application developers/designers, clinicians/interventionists, and researchers. Implications for Developers and Designers In 2006 over 400 self-injury online communities were documented (Whitlock, Powers, & Eckenrode, 2006) and a 2018 review identified 27 mobile applications specifically marketed to individuals who self-injure (Vieira & Lewis, 2018). This plethora of online resources for individuals who self-injure is promising in terms of the recognized need; however, the efficacy of these tools for improving mental health 165 and decreasing self-injurious behaviors is largely unknown (Vieira & Lewis, 2018; Lui, Markus, & Barry, 2017). The issue with developing and validating the efficacy of these platforms is complex and requires collaboration among researchers and developers. The research undertaken as part of this dissertation contributes to a growing effort to rigorously examine the relationship between online activities and self-injury outcomes and would not have been possible without the continued support and dedication of the founders of the TalkLife application. In the following section, I describe features that individuals take advantage of when they are engaging with TalkLife and make recommendations for future development where appropriate. Then, I elaborate on two opportunities to augment the application including (1) expanding the app’s private functionality and (2) ensuring receipt of support, in more detail. While my focus is on TalkLife, I believe that the implications discussed here can be useful for other platforms aimed at supporting individuals self-injure. Nevertheless, the findings from this dissertation should be treated as preliminary and future work should ideally replicate these findings on TalkLife and explore similar patterns on other online peer platforms. What is (and is not) Currently Being Utilized? Anonymity. While anonymity was not used frequently (15% of users), it did appear to serve a purpose for some users. Specifically, individuals who were on the application for a longer period of time, chose to post anonymously at higher rates than newer members. In line with theorizing and qualitative interviews with individuals who use online communities for stigmatized conditions, features which allow users to 166 flexibly engage in impression management are appreciated (Walther & Boyd, 2002; Andalibi, Morris, & Forte, 2018; De Choudhury & De, 2014). Despite concern over the role of anonymity in negative disinhibition (Suler, 2004), I noted very few instances of negative feedback from the TalkLife community and I did not observe patterns to suggest that anonymity contributed to these cases. Moderation. Social applications that involve discussions of sensitive topics often benefit from moderation (Whitlock et al., 2006; Webb, Burns, & Gollin, 2008; Kendal, Kirk, Elvey, Catchpole, & Pryimachuk, 2017). TalkLife includes a number of features which allow users to customize their experience and protect themselves from viewing potentially triggering content. In chapter 2, I found that nearly half of the users identified a specific set of triggers with self-harm content being the most common, followed by sexual, violence, and eating disorders. Publishing triggering posts. In addition to selecting what types of content users find triggering (so as to filter this content from their main feeds), they can also add trigger warnings to content before publishing. I found that trigger warnings were commonly used, and their use is an important predictor of self-injury outcomes. For example, posting triggering content was associated with increased likelihood of engaging in self-injury behaviors and having self-injury thoughts. This relationship suggests that it may be worthwhile to engage users when they post triggering content. TalkLife could consider options to automate support for these users, or to engage moderators when triggering content is posted, if this is not already implemented. One reason an automated approach is attractive is that community members may be less likely to engage with this type of content for their own 167 protection. In chapter 3, for example, I found that users were slow to respond to posts mentioning death, though I did not conduct a thorough analysis of whether these posts were also marked as “triggering.” Another option could be to provide the audience with a bit more context about the triggering content so they can choose to send feedback without having to view the content themselves. Currently users can send hearts without reading the content—but they may feel some hesitation to do so without knowing what it is they are supporting. Dismissing trigger warnings. I also found that individuals choose to dismiss trigger warnings to view content. Doing so was related to a greater ability to resist self-injury urges and greater intentions to injure. While my analysis does not afford causal conclusions, this finding may suggest that viewing triggering content diminishes self-injury urges for some or may impact intentions to injure. Again, the directionality of this relationship merits attention in future work but if a temporal analysis found either of these to be the case there would be design implications to consider. For the prior, the current TalkLife design may be optimal. Indeed, popular social media sites are currently revising their moderation of potentially triggering self- injury content to include features similar to the trigger dismiss option in TalkLife (e.g., Instagram sensitivity screens; Carman, 2019). If future work, finds that viewing triggering content is associated with subsequent increased intention, however, then it may be important to make it more difficult to view triggering content by increasing the number of actions users take to view the post. In sum, the findings related to moderation and triggering content (both posting and viewing) imply that users flexibly engage with such content. Collectively, these 168 findings illustrate the importance of building in choice when designing platforms for individuals who self-injure. Removing triggering content altogether on social platforms is contested (See “Implications for researchers” section for further elaboration) because it can lead to further feelings of invalidation (Easton, Diggle, Ruethi-Davis, Holmes, Byron-Parker et al., 2017). TalkLife provides users with many opportunities to flexibly engage with a variety of content and it seems they appreciate and take advantage of these opportunities. Diary. The diary feature was not used frequently. In fact, it was used by only 3% of the users from Chapter 2. Descriptives of this set of users suggest that they are predominately female, slightly younger than the average user, and are moderately or highly engaged on the application. While TalkLife is built around its social functionality, I believe that it is worthwhile to explore ways to expand the private functionality of the app – the diary feature being one key target. Several recommendations follow in the section “Augmenting the Experience.” Mood checklist. All posts on TalkLife are associated with a mood. I found that the mood checklist was an important predictor for both the amount of reactions posts received and response time. Posts with negative and ambiguous moods received fewer reactions and a quicker response, than posts associated with positive moods. Based on past research, I suspect that users may look to the mood checklist for contextual cues about the emotional state of a poster and to evaluate their need for support (High, Oeldorf-Hirsch, & Bellur, 2014). TalkLife users may ascribe greater urgency to posts conveying negative or ambiguous affect, for example. Notably, positive or negative affect in language did not predict response time. Thus, the mood checklist appears to 169 tap into something beyond the expressed affect in post content. Given that these moods received fewer reactions, it may be that individuals see the primary purpose of the reaction feature as expressing agreement or acknowledging individuals who are positively contributing to the community. Given that posts with negative or ambiguous moods received fewer reactions, it may be worthwhile to think of ways to incorporate additional features to increase the amount of feedback individuals receive. For example, it may possible to include additional signals such as a needs or goals checklist to convey a sense of urgency without further taxing the poster or requiring additional disclosures. This checklist could facilitate support seeking and include options ranging higher need (e.g., “In need of support!” “Would like feedback”) to lower need (e.g., “Wonder if anyone can relate?” “Just getting things off my chest.”). Augmenting the Experience Research on designing technologies for individuals who self-injure is limited; however, some useful insights can be drawn from published work describing or testing prototypes (Birbeck, Lawson, Morrissey, Rapley, & Olivier, 2017; Hetrick, Robinson, Burge, Blandon, Mobilio et al., 2018; Grist, Porter, & Stallard, 2018). Several studies have consulted with individuals with self-injury experience and recommend that technological tools provide users with opportunities for distraction, social support, and increasing coping strategies and self-insight (Birbeck et al., 2017). TalkLife has many features which can work towards this end, but I believe that they could be better optimized to meet the needs of the community. Of course, the recommendations that I make here are based on findings from this dissertation and qualitative data from other 170 studies, so they should be treated as preliminary. Prior to making any changes it would be advisable to directly involve members from the TalkLife community in the research process to identify what needs are, and are not, being met and describe what they feel could augment their experience. In what follows, I make several recommendations on how to expand the private functionality of the application and facilitate the receipt of support below. Expand the private functionality of the app. While the social functionality of TalkLife is paramount to its success as a social network, the application has private functionality that appears to be underutilized (e.g., diary). Platforms like TalkLife have the unique opportunity to support individuals who have not disclosed their self- injury elsewhere, or may not have access to formal treatment, so it is worthwhile to think of ways to design environment to support the development protective factors like self-compassion, self-awareness, and to provide opportunities for users to build/practice coping skills (Xavier, Pinto-Gouveia, & Cunha, 2016; Klonsky & Muehlenkamp, 2007) (1) Augmented diary: Scholars have noted the potential for integrating diary- centric tools in applications designed to support individuals with mental health conditions (Andalibi, Ozturk, & Forte, 2017; Chancellor, Mitra, & De Choudhury, 2016). A rich body of literature has shown the beneficial effects of expressive writing and journaling on mental health and well-being (Travagin, Margola, Dennis, & Revenson, 2016; Baikie, Geerligs, & Wilhelm, 2012). In the context of self-injury, a recent study showed that daily expressive writing (either about worries or positive reflections) was associated with reductions in self-criticism and self-injury episodes 171 (Hooley, Fox, Wang, & Kwashie, 2018). Integration of expressive writing in existing social platforms has been limited; however, one study provides promising evidence for its efficacy. For example, Lee and colleaguges (2016) built a diary interface built on top of Facebook and found that it was well-received in terms of usability and efficacious in diminishing depressive moods in a sample of users with sub-clinical depression (Lee, Kim, Yoo, Park, Jeong, & Cha, 2016). The TalkLife diary presents users with a space to record their thoughts and feelings, but it is currently used infrequently. Lack of use could be for a variety of reasons including disinterest or uncertainty around how to engage with this feature. I believe that several changes can better encourage use of this space. First, while the TalkLife offers guidance on how users should conduct themselves in public or shared spaces, I am not aware of similar guidance on how users may take advantage of the diary feature. In light of the potential benefit of this writing space, I recommend that TalkLife introduces the functionality of the diary to users when they first create an account. Simply adding a few lines to show users that they can write diary entries or save posts or user comments to their diary may be sufficient. Second, the diary feature could be augmented to include a selection of optional structured prompts. These prompts would serve as models for how users might engage with the feature and could encourage writing that has proven to be of value clinically (e.g., self-reflection, emotion processing, or cognitive restructuring) (Sloan & Marx, 2004). (2) Design to enable self-monitoring: An additional direction to expand private aspects of TalkLife is to provide users with information about their use patterns over time thus increasing self-knowledge and enabling self-management. 172 Self-monitoring has proven to be useful for individuals with various health conditions, including depression (Hetrick et al., 2018; Barlow, Wright, Sheasby, Turner & Hainsworth, 2002). Studies have shown that individuals with mental illness are interested in using mobile phone applications to track their symptoms (Torous, Chan, Yee-Marie Tan, Behrens, Mathew, Conrad et al., 2014; Walsh, Golden, & Priebe, 2016). Mood tracking, one form of self-monitoring, is a target in several therapies for self-injury including dialectical behavior therapy (DBT; Linehan, 1993; Nahum et al. 2017; Church et al., 2010) and in an HCI study individuals who self-injure reported deriving benefits from being able to quantify and visualize patterns in moods overtime (Birbeck et al., 2017). Given that TalkLife already has the mood checklist, I see potential to render this information on a timeline and send it back to the user for their reference. If users find this information helpful, other ways of meaningfully rendering information to support users’ awareness of patterns, may be considered. For example, a possible extension of this self-monitoring suite could be to provide users with information on how much time they spend in various categories, where they direct their attention, and how this attention is related to their emotional experience (moods) over time. Metrics on interpersonal dynamics may similarly be considered as a means of building and sustaining strong personal bonds. For example, providing users with a sense of who they most often engage with, and how long it has been since they last exchanged support, may encourage reaching out, checking in, and reciprocity among users. (3) Skill-building: As previously mentioned, TalkLife, and other apps, are often inhabited by individuals who are underrepresented or are less likely to seek 173 therapy for self-injury (e.g., males, non-binary young people). I found that while the TalkLife samples I examined are heavily female, 13% of the sample in chapter 4 identifies as transgender or non-binary and males represent between 31 – 35% of the sample in all chapters. Again, in an effort to optimize users’ experience on the application, I suggest developing a resource bank where individuals can access skill building activities and coping strategies. In my qualitative analysis of the TalkLife content in chapter 2, I saw several posts with a list of useful distraction techniques and coping strategies. Posts like these were re-posted by community members presumably to increase visibility. In keeping with participant-centered design techniques, it could be worthwhile to create a moderated space where users can suggest and link to resources that they have found to be helpful in their own journeys (e.g., activities, materials). Alternatively, TalkLife could incorporate psychoeducational materials, therapeutic strategies, and tools to increase efficacy within the platform itself. Others have similarly recommended the incorporation of didactic information in mental health apps (Baumel & Schueller, 2016). The moderated online social therapy (MOST) model can provide useful guidance on how to integrate skill-based tools in a social environment (Lederman, Wadley, Gleeson, Bendall, & Alvarez-Jimenez, 2014; Gleeson, Alvarez-Jimenez, & Lederman, 2012). Websites designed with the MOST model often include public spaces where individuals can engage with moderators and peers and private spaces where they can do interactive psychoeducation modules and skill-building activities (Rice, Gleeson, Davey, Hetrick, Parker & Lederman et al., 2016; Gleeson et al., 2012; Ridout & Campbell, 2018). In a systematic review of 174 social networking and mental health interventions, intervention spaces based on “MOST” model received positive feedback from consumers (Ridout & Campbell, 2018). Facilitating receipt of support. Another implication to emerge from my research relates to the receipt of support. While the majority of posts analyzed as part of this dissertation received some feedback from the community (either through comments or reactions), there were posts that did not receive feedback and patterns around what types of posts received more or less feedback. Because individuals who self-injure frequently report feelings of invalidation or being misunderstood as barriers to recovery (Kruzan & Whitlock, in press; Brown & Kimball, 2013; Choate, 2012), it is critical to ensure that users receive acknowledgement and support when they choose to disclose. Findings from chapter 3 can be informative when thinking of strategies to improve feedback and to ensure that posts in need receive attention. (1) Encouraging direct requests: I found differences in the amount and type of feedback direct and indirect posts receive from the community (Liu, Kornfield, Shaw, Shah, McTavish, & Gustafson, 2017). Indirect posts were more common than direct posts and were associated with fewer comments and more emotional support. Direct posts, by contrast, received more comments and fewer reactions. Similar findings have been reported in other reserach suggesting that individuals feel more compelled to comment on posts when there is an explicit request, or the individuals need is easy to discern (Liu et al., 2017). In chapter 3 I noted that indirect posts may receive less feedback because it is difficult to discern when a poster is expressing themselves for personal benefits (e.g., personal record, catharsis) or is in need of 175 community perspectives (Andalibi et al, 2018; Greene, Derlega, & Mathews, 2006). In these cases, the burden of interpretation falls on the audience and this may lead to low or no response. To facilitate receipt of support, it may be useful to consider ways to encourage users to be more direct with their requests when they are looking for feedback. TalkLife could publish recommendations on how to request help from the community in community guidelines. For example, the application could simply suggest that users write in a direct manner when they are seeking information or someone to engage with immediately and provide some examples. It may also be possible to detect patterns at the individual level and send a direct message with tips on how to better engage the audience to meet their needs after users have met a certain threshold (e.g., after 4 indirect posts that received less than 2 comments). As a word of caution, prior work has shown that indirect posts can serve an important purpose for individuals with stigmatized conditions and that these indirect posts can be strategic (Andalibi, Haimson, De Choudhury, & Forte, 2018). Any design changes should consider how to balance providing suggestions based on what has been evinced in the data, while also allowing users to post indirectly without interfering or turning them off to the application altogether. (2) Directing attention to posts that have not received comments and ensuring timely response to sensitive disclosures: A non-trivial portion (~ 20%) of posts did not receive comments. However, preliminary language analyses did not reveal significant differences between posts that did or did not receive comments. Results from chapter 3 suggest that it may be a combination of other factors at 176 multiple levels which influence receipt of support. Given the importance of feel heard, I would suggest considering ways to better direct attention to posts without feedback. I found that the number of comments a post receives is related to the directness of the post (as describe above) and increases with user engagement. First, posts could similarly be identified when they appear to be in need of a specific type of support algorithmically. Users who post distressed comments (particularly those that use indirect solicitation strategies) might be flagged and receive the attention of a moderator or a volunteer who can provide them with support, for example. An extension of the work presented in this dissertation, which is currently underway, is to apply machine learning to identify direct and indirect posts. When applications are able to detect solicitation types, it may then be possible to design interventions aimed at matching solicitations and response. Second, when considering resource allocation, the timeliness of peer response may also have an impact on the benefit users derive from the exchange of peer support. I found that more established users (user tenure) received a quicker response to their posts but generally fewer responses (comments and reactions). This suggests that it takes longer for newer members to received feedback on their posts. Past work indicates that first-time disclosures, among presumably newer users, are often support- seeking in nature and the response users receive to these early posts can have an impact on their future attitudes and disclosure behaviors (Andalibi, Haimson, De Choudhury, & Forte, 2016). Thus, while it is promising that newer users typically receive more feedback on their posts, we should also consider ways to ensure timely delivery of support. Displaying a user’s tenure a bit more prominently on the user 177 profile, so as to indicate when individuals are new to the site, may help encourage welcoming behaviors and quick responses these posts. In summary, several recommendations that can be drawn directly from the findings of this dissertation. It would be advisable for application developers to collaborate with key stakeholders including users who have lived-experience with self-injury and clinicians before implementing these changes (Vieira & Lewis, 2018; Owens, Farrand, Darvill, Emmens, Hewis & Aitken, 2010; Hetrick et al., 2018; Stallard, Porter, & Grist, 2018). Research has shown that actively engaging with peers online about mental health concerns is associated with an increased likelihood of seeking formal mental health care (Dyson, Hartling, Shulhan, Chrisholm, Milne, Sundar et al., 2016; Ridout & Campbell, 2018). Thus, these spaces have the potential not only to provide immediate support and feedback but can have stake in their users’ future help-seeking and healthcare involvement. While peer support through the community is the most obvious use of this venue, I have made suggestions on some alternative ways of utilizing existing features to augment the overall experience. These design ideas emerged from my studies of individuals who discuss self-injury on TalkLife and were informed by past work on mobile applications and online communities with self-injuring individuals. Implications for clinicians and other mental health professionals Online venues are evolving resources for individuals with mental health concerns and their use has implications for clinical practice. While many individuals in online communities may not be involved in formal treatment, in Chapter 4, I found that around 42% of participants reported having been in therapy at some point 178 throughout the study. Clinicians should thus be aware of their clients’ online activity and consider how online resources can be an adjunct to formal treatment. Several studies have shown that online peer support platforms can be a feasible and beneficial addition to existing treatment plans. For example, 7Cups, a peer support application that relies on volunteers who receive training in listening and empathy, has been adapted and integrated in treatment plans for a variety of different mental illnesses including postpartum depression (Baumel, Tinkelman, Mathur, & Kane, 2018), schizophrenia spectrum disorders (Baumel, Correll, & Birnbaum, 2016) and perinatal depression and anxiety (Baumel & Schueller, 2016). Preliminary results from a nonrandomized study with women experiencing postpartum depression, suggest that 7Cups use may enhance treatment outcomes (e.g., lower depression scores; Baumel, Tinkelman, Mathur, & Kane, 2018). Further, patients receiving treatment for schizophrenia spectrum disorders and perinatal depression and anxiety reported feeling satisfied with the additional support provided through the application (Baumel, Correll, & Birnbaum, 2016; Baumel & Schueller, 2016). Clinicians evaluating the platform offered guidance on when it would be appropriate to refer clients to 7Cups. In general, they felt it could help their patients socialize and feel supported between sessions, but that it might not be helpful for those who already have a strong support network, clients who are suicidal, or clients who are actively psychotic or manic (Baumel, Correll, & Birnbaum, 2016; Baumel & Schueller, 2016). Clinicians also recommended that the peer support providers (called “listeners”) receive brief training in the mental health condition they would most likely be providing support for (Baumel, Correll, & Birmbaum, 2016). These studies 179 offer a good model for how clinicians and researchers can work together to evaluate and implement technologies in treatment. Similar evaluations and guidance could be drafted for TalkLife and other applications. Even when a therapeutic approach does not include an online component, it is beneficial for clinicians to be aware of their clients’ online activity. If clinicians do not acknowledge their clients’ online activity, they are at risk of overlooking critical aspects of their social environment and sources which inform their clients’ perspectives on self-injury and recovery. Several papers have provided guidance on probing for online self-injury related activity in client sessions (Whitlock, Lader, & Conterio, 2007; Duggan, Heath, Lewis, & Baxter, 2012; Lewis, Heath, Michal, & Dugan, 2012). For example, Whitlock, Lader, & Conterio (2007) propose a series of questions to assess the functions of online activities, the degree of client involvement, and the perceived effects on offline behaviors (e.g., “How often do you visit the Internet to get or share health information?” “Are there places you regularly go to find out about or talk about self-injury?”). It may also be useful to monitor risks and benefits through functional assessment over time (Lewis et al., 2012). When a client’s online activity appears to be impeding on the recovery process then it might be necessary to implement interventions aimed at modifying use or have conversations highlighting how certain patterns are having a negative influence on clients’ mental health (Lewis et al., 2012). Based on my findings, some patterns which may merit attention include client’s activity level (with more activity associated with less likelihood of engaging in self- injury behaviors) and their self-moderation strategies (posting or dismissing trigger 180 warnings). It may also be worthwhile to consider how strongly clients identify with others in the community and how they feel following visits to the community. For mental health practitioners (such nurses or those in school settings) who encounter individuals who self-injure only once, a robust assessment of online activity may not be feasible. However, it is still critical to take advantage of these brief encounters through providing individuals with additional resources. Duggan et al. (2012) proposed an assessment tailored to one or limited-time encounters with injuring youth. The researchers suggest concluding sessions with a fact sheet detailing professionally driven, credible informational websites for information seeking (e.g., Cornell Research Program on Self-injury and Recovery (http://www.selfinjury.bctr.cornell.edu) and the Self-injury Outreach and Support (http://sioutreach.org/about-sios/). Unfortunately, it would be difficult to refer clients to specific empirically supported online communities or mobile applications, given the state of current research (Lui et al., 2017). Findings from this dissertation provide initial evidence for some positive effects of TalkLife, however, the field is in need of research examining longer term outcomes through randomized-controlled trials and other rigorous longitudinal assessments. Mental health professionals can take a brief inventory of their client’s online involvement and offline social support and use good discernment in choosing whether to bring online communities or applications into the conversation as a potential resource. In sum, the prior work suggests that clinicians should (1) be aware of the different technologies their clients may use, (2) understand their use patterns and what 181 types of content they are encountering, and (3) in the event that the online community seems to be a negative influence, provide alternative resources. Additionally, online communities may be best for individuals lacking offline support (Baumel, Correll, & Birmbaum, 2016). My own data on TalkLife provides evidence for a rich exchange of support and information on self-injury coping; however, I also note an overwhelming sense of hopelessness in posts on the site. for individuals who are particularly vulnerable or at early stages in the behavior change or recovery process, online communities, like TalkLife, may exacerbate distress. I conclude with a summary of several key take-aways drawn directly from the findings of this dissertation and extant literature on self-injury and online activity below: (1) Clinicians should not be fearful when clients report engaging in online activities related to self-injury. Research has shown that online communities can have benefits and risks and this dissertation describes a number of patterns that are linked to self-injury outcomes. Importantly, findings from Chapter 4 suggest that exposure to triggering content does not increase self-injury behaviors or thoughts. While these findings are preliminary, they do suggest that conventionally “risky” activities may not always be harmful. What is perhaps most important is for clinicians to be aware of such activities and to respond to their clients in an open and non-judgmental manner. (2) When analyzing the content on TalkLife, I found that many disclosures were accompanied with negative emotion and dealt with complex life experiences. These posts were often met with support and validation from peers who have had 182 similar life experiences. It is important for clinicians to recognize that this type of peer support can be an asset and is not something that can be easily transmitted through a therapeutic setting due to relational asymmetry. The relationships developed in online communities can result in clients feeling understood and less alone. Particularly for clients who do not have a strong support system offline, their use may be valuable. (3) Having an awareness of clients’ online activity can not only enrich the therapeutic experience but may also make the client’s experience of online use better. Some work suggests that applications work better when they are paired with live support or are an adjunct to more traditional therapy (Chandrashekar, 2018). (4) Increasing client’s awareness of their online activity may be a way of gaining additional insight into their recovery process. While it can be difficult to look at fluctuations in moods or link moods to specific events, online engagement leaves a digital trail that can be reflected back on in a therapeutic setting. Ethical challenges regarding the use of client data, even when voluntarily offered, in sessions must be considered. (5) There are a number of credible information sites that can be recommended to clients; however, further research is needed to confirm the benefit of online communities and mobile applications. It would not be advisable to recommend any one specific naturally occurring online community for clients at this point. However, clinicians may cautiously consider recommending online support to clients who do not have support, or are hesitant to disclose their self-injury, in offline settings. 183 (6) When clients report online activity related to self-injury it may be beneficial to encourage certain use patterns, depending on client needs. Specifically, if clients are in need of emotional or informational support, direct solicitations appear to generate a greater volume of feedback. Other clients may reap benefit from simply feeling less isolated or alone. Assessing risks associated with online activities such as behavior normalization and whether online communities have a recovery- orientation and moderation will be important. Implications for Researchers and Areas for Further Research Naturally-occurring online data can be a resource for researchers interested in understanding and helping individuals who self-injure. The disclosures individuals made on TalkLife provided insights on the opportunities and barriers experienced as a result of self-injury can be used to predict self-injury outcomes. In the following section, I discuss how the findings from this dissertation can be used in future research and outline relevant ethical and methodological questions for researchers working at the intersection of technologies and mental health. Key Factors Few studies have considered how to predict self-injury behaviors through naturally occurring social media data (with exception to Soldaini, Walsh, Cohan, Han & Goharian, 2018) and most research on online activities related to self-injury, has been descriptive (Lewis & Michal, 2016; Lewis & Seko, 2016; Rodham, Gavin, Lewis, Bandalli, & St. Denis, 2016). By investigating specific dynamics and use patterns on TalkLife, I extend this past work to identify factors which predict several outcomes related to self-injury and peer responsiveness and hope these factors can be 184 used future work. All significant predictors from chapters 3 (peer support) and 4 (self- injury) can be seen in Table 21. Importantly, model performance was improved when adding features at multiple levels including individual characteristics, language dimensions in messages, and platform affordances – emphasizing the need for future research to take a socioecological perspective and account for multiple layers of influence (Weiss, Berner, Johnson, Guise, Murphy, & Lorenzi, 2013). While language characteristics have been central to past work when building algorithms to detect mental health (Chancellor, Lin, Goodman, Zerwas, & De Choudhury, 2016; Sharma & De Choudhury, 2018), they were not particularly strong predictors in my models. In fact, the most consistent predictors were individual characteristics and variables related to use patterns (e.g., engagement, trigger posts, activity). The results of this dissertation also demonstrate the value in considering raw behavioral and language data as well as measures of individual change over time (e.g., lability, rate of change). Emotional lability, a common characteristic among individuals who self-injure, emerged as an important factor predicting self-injury thoughts and ability to resist urges in chapter 4, for example (Santangelo, Koenig, Funke, Parzer, Reisch, Ebner-Priemer et al., 2017; Anestis, Silva, Lavendar, Crosby, Wonderlich, Engel & Joiner, 2012). In light of my findings, and other work using computational methods to discern mental health status, it will be important for future research to include robust indices that capture individual fluctuation. Indeed, a key challenge surfaced at a recent Technology for Early Awareness of Addiction and Mental Illness meeting (2018) was that digital signatures can vary greatly (Baumel, Baker, Brinbaum, Chrristensen, De Choudhury, Mohr, Muench et al., 2018). The 185 proposed solution was to focus on “individual changes over time rather than only on patterns relative to group norms” when evaluating and monitoring mental health (Baumel et al., 2018, p. 591). In sum, future research interested in either building algorithms to detect self-injury and/or identify gaps or intervening in the peer support process may use the factors in Table 21 as a point of departure. Table 21 Significant Predictors by Outcome Outcome Significant predictors Peer support comments User tenure, engagement, directness of post, mood valence reactions User tenure, directness of post, mood valence response User tenure, engagement level, mentions of urges and time relapse, “biological processes” language, post category, mood valence Self-injury behavior Age, race, trigger posts, difference in trigger dismiss thoughts Race, activity, trigger posts, difference in trigger posts, variance in rumination Ability to Race, trigger dismiss, “I” language, “efficacy” resist urges language, difference in positive emotions, variance in trigger dismiss, variance in anger intentions Age, race, activity, trigger dismiss, “family” language, difference in trigger dismiss Frequency of Age, trigger posts, “I” language behavior Research Challenges Using online data to guide interventions such as those mentioned above gives rise to a number of ethical and methodological challenges which merit careful attention from scholars in various technical (e.g., Communication, Computer Science, Human-Computer Interaction) and clinical disciplines (e.g., Clinical Psychology and Social Work). Ethical concerns around how to balance user privacy and safety are 186 noted (Wright & Javid, 2019; De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016). I explore several challenges for practice and policy concerning moderation and intervention in what follows. Ethics and moderation. At the time of writing this dissertation, tensions around moderation and free expression were salient in both public and academic discourse. Policies around self-harm content, in particular, were in flux following the suicide of a 14-year-old girl (Molly Russell) in the United Kingdom (Carmen, 2019). Russell’s father claimed that Instagram may have played a role in her suicide after finding that she was regularly viewing graphic self-harm content (Carman, 2019; Hern, 2019; Oakes, 2019). This event gave rise to a government white paper discussing new policies around “ethical technology,” and how platforms should handle and report self-harm and suicidal content in the UK (Wright & Javid, 2019), as well as global changes to Instagram’s moderation of self-harm content (Carmen, 2019). Specifically, new provisions on Instagram allow users to talk about suicide and self-harm so long as they do not actively promote it and new algorithms reduce the searchability of such content through hashtags (Carman, 2019). Instagram also adopted sensitivity screens, similar to TalkLife “trigger warnings,” which blur images of self-harm so users must tap the image to view the content (Carman, 2019). However, it has already become clear that users simply modify hashtags (e.g., self harmmm) in order to keep their communities alive – suggesting the value of these communities for their members and calling to question the viability of these moderation efforts. Moderation appears to be a beneficial component of online communities which 187 discuss sensitive topics; however, it is important that social platforms do not react to such events by overly safeguarding or closing down rather than launching empirically informed interventions. For example, a brief report describes that heavy moderation can unintentionally lead to more harm (Lavis & Winter, 2019). Posting graphic content can be a way to reach out for help and these expressions are often effective in soliciting recovery-oriented support and compassion (Lavis & Winter, 2019; Lewis et al., 2012). Thus, removing this content may also remove a potentially critical source of expression and support for users. This dissertation further demonstrates the important role of triggering content in the overall experience of the TalkLife platform (chapter 4) and suggests that individuals do self-moderate and take advantage of moderating features. While moderation may seem like a consideration for designers, I argue that this ethical dilemma is really something to be addressed in collaborations with researchers, designers, and others. Researchers are uniquely positioned to understand and assess what levels of moderation are most beneficial to users. What qualifies as an appropriate amount of moderation (or appropriate safeguarding) will likely depend on community norms and the types of information users commonly exchange. Since the compositions of these communities are dynamic (e.g., new members register and others depart simultaneously) the most effective safeguarding would be responsive to shifting community norms and environmental influences (e.g., suicide in the media, etc.). Of course, this approach would require a significant amount of oversight and coordination between application developers and researchers and infrastructures would need to be in place to support such a dynamic approach. For researchers in this 188 domain, finding creative and controlled methods to study moderation will be an important contribution. Ethics and risk interventions. Computational approaches to social media data analysis have been used to detect mental health status and risk (De Choudhury et al., 2016; De Choudhury et al., 2013). Social media platforms, like Facebook, have begun to build algorithms to detect suicide and self-harm making answers to questions such as: (1) how accurate are these algorithms in detecting risk? (2) under what circumstances is it appropriate to intervene? and (3) what types of interventions are most valuable? of imminent importance. For example, earlier this year concerns about Facebook’s suicide risk algorithm (originally launched in March 2017) received academic, legal, and public attention (Goggin, 2019). Facebook developed algorithms to classify posts as having suicide and self-harm risk, assigned risk scores to these posts, and then initiated interventions when suicide risk scores were high (Goggin, 2019). These interventions ranged from sending distressed users’ messages with links to resources, to dispatching emergency personnel to the homes of users and having them undergo psychological assessment (Goggin, 2019; Singer, 2018). Suicide prevention efforts are critical; however, we need to understand the consequences of these interventions and find the right balance between protecting users’ privacy and safety. Given the prevalence of suicide among young people (Van Meter, Paksarian, & Merikangas, 2019), the potential of being able to intervene in time is promising. However, we are still in the infancy of being able to detect and provide useful interventions on sites like TalkLife. As others have noted, it will be important for researchers to consider how to translate findings from sophisticated 189 detection algorithms into practice and to have a set of guidelines for developing, and validating, digital interventions (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016). Ethics and positive interventions. While most of the current work using computational methods has focused on detecting mental health risk (e.g., De Choudhury et al., 2016; De Choudhury, Counts, Horvitz, & 2013), these same methods can be employed to facilitate greater connection and to encourage the therapeutic use of platforms. For example, in chapter 3 I identified several individual, message, and platform characteristics that predicted peer responsiveness (See Table 21). I suggested that these factors can be meaningfully employed in future work to guide algorithms capable of detecting where resources are needed, and to increase peer responsiveness. Similarly, Pruksachatkun, Pendse, & Sharma (2019) employed machine learning techniques to identify moments of cognitive change in threads on TalkLife. In-so-doing the authors learned what patterns lead to these desirable moments and provide guidance for application developers on how to design and intervene to support these moments of change. While there have been discussions on whether it is appropriate to intervene under extreme circumstances (e.g., suicide; Conway & O’Conner, 2016; O’Dea, Wan, Batterham, Calear, Paris & Christensen, 2015; Lehavot, Ben-Zeev, & Neville, 2012), I argue that similar conversations about ethics need to be had regarding whether it ethical and/or beneficial to deliver interventions facilitating positive interventions (e.g., encouraging the exchange of support, directing resources to posts not getting attention). In some ways these interventions seem less risky, but research has shown 190 that even small changes to social media data can have an impact on the emotional state of its users (Kramer, Guilory, & Hancock, 2014). Moving forward, conversations on ethics and algorithm transparency are critical. Methodological challenges and future research directions. Several methodological challenges became salient, particularly when completing the last empirical chapter of dissertation (chapter 4), and I believe these have implications for future research. Longitudinal social media data with existing users. Longitudinal studies frequently suffer from problems of recruitment and attrition, and I found this to be the case for chapter 4 (Arigo, Pagoto, Carter-Harris, Lillie, & Nebeker, 2018). In chapter 4, I administered surveys entirely administered through the TalkLife platform in order to track individuals who were already who were already active members of TalkLife. This component of the study design has strengths and limitations. In doing so, I was able to capitalize on some of the existing classifiers being used on the site to identify individuals who self-injure, and I was able to collect data in a relatively unobtrusive way. While this method provided excellent external validity (in that the samples were pulled from an existing community of users), it also introduced some sampling bias. Individuals who chose to respond to the surveys, for example, may have been more active on TalkLife and more invested in self-injury behavior change, relative to those who chose to decline. Additionally, in order to complete weekly surveys, participants must have logged into TalkLife at least once a week. Recall that surveys were issued to TalkLife users who had been previously identified as having discussed or engaged in self- 191 injury. These users were flagged and prompted to answer surveys weekly for the duration of the study period (2 months). However, very few users answered more than 2 surveys. Furthermore, many individuals who were prompted to answer survey questions did not do so regularly leaving uneven intervals between survey sessions. Based on the response rates, I suspect that participants either tired of receiving these surveys, or they were not active enough to gain access to the surveys weekly. It would be worthwhile for future research to consider how to maximize the potential for obtaining a robust longitudinal sample of existing platform users, without prescribing use. Push notifications are one design feature to consider—however, if push notifications come from the application itself it may artificially prompt activity. Mapping offline and online behaviors. Relatedly, perhaps one of the greatest challenges I encountered was assessing how to best combine naturally occurring log data with survey data to get precise estimates of activities that came before and after self-injury events (e.g., thoughts, behaviors). My survey questions were framed to ask about any self-injury in the week prior but did not ask about specific days, or times of day, when these events occurred. In sum, I could not discern whether the log activities occurred before or after the self-reported events (e.g., self-injury behaviors, thoughts, urges, etc.). Anchoring surveys to actual self-injury events would present difficulties as it can be intrusive and taxing for individuals to self-motivate and record these behaviors when they are in the midst of experiencing distress. However, daily surveys, either through diary or ecological momentary assessments, could provide a more nuanced picture of the temporal relationship between variables. With further research, it may also be possible to detect self-injury occurrences 192 algorithmically, and automatically issue surveys assessing relevant predictors including contextual information that is not discernable from the online data itself. These factors may include location (e.g., home, school) and social presence (e.g., with family, friends) at time of injury, and leading up to these occasions to provide further context for researchers. It would similarly be useful to obtain information on when participants access their mobile devices and when – relative to self-injury activity – they navigate to these platforms for resources. Such research could identify antecedents and precedents of self-injury and provide more nuanced understanding of why and under what conditions users access online applications, or other media sites. Ultimately, this type of research would enable the design of individually tailored interventions or targeted messaging to be delivered when individuals display patterns that make them vulnerable to injuring. Need for rigorous multi-method testing. As previously mentioned, clinicians recognize the potential for technology to support the therapeutic process; however, they are currently unable to confidently recommend applications or online communities due to lack of empirical support (Liu et al., 2017; Mohr, Weingardt, Reddy, & Schueller, 2017). For example, in a survey study about ¼ of providers said they were interested in using and recommending mobile applications in their practice (Schueller, Washburn, & Price, 2016). The field is in need of rigorous, longitudinal studies assessing behavioral and cognitive outcomes related to self-injury online activity. Given the dearth of empirical support for the impact of applications and online communities on self-injury outcomes, I feel this effort should be prioritized above efforts to develop or design new applications. 193 A multi-methodological, cross-disciplinary effort to validate existing platforms should be undertaken. Randomized-controlled trials are ideal for evaluating the impact of treatments (such as use of a mobile app); however, they are resource-intensive, costly, an involve sacrifices to external validity due to their highly controlled nature (Sanson-Fisher, Bonevski, Green, & D'Este, 2007). Methods like those used in this dissertation have high external validity (since they are naturally occurring) but do not enable tight control over other environmental or contextual factors. Usability testing, or studies involving participatory design, both require significantly smaller sample sizes than computational methods or RCTs but introduce desirability bias and it is often difficult to navigate motivations of key stakeholder groups (Stiles-Shields, 2018; Lewis, 1994; Tullis & Albert, 2008). Moving forward, it will be useful to unite different disciplines with specialties in these specific designs and to think strategically about how and when we should employ these designs at various stages. For example, participatory design and usability testing make sense as an initial step to assess the value of the new technology, whereas RCTs and computational methods may be used to assess how applications or online communities are or are not meeting the community’s needs and impacting key outcomes. Other directions for future research. Perhaps the greatest need for future research to examine the temporal relationship between online activities and behavior change, as addressed above (Arigo et al., 2018). We similarly need to better understand which mechanisms contribute to desirable change. One valuable direction forward is to conduct in-depth interviews on platform use to help discern what aspects 194 of use are perceived as most important and contribute most to helping users achieve their goals. Qualitative work focusing on existing users may help discern decision- making frameworks and identify areas for further development (e.g., Andalibi, Morris & Forte, 2018). Another promising direction for this work, which is currently underway, is to use the results from this dissertation (particularly those from chapter 2) to design a randomized controlled trial of TalkLife with individuals who self-injure. This RCT will address some of the limitations of this dissertation and will be able to more closely assess causality and shifts in mechanisms that are theorized to be important (e.g., belongingness, stigma) in the behavior change process. In addition, this RCT aims to discern how online support, through the use of TalkLife, relates to other help- seeking and professional care, over time – an area that is need of further research (Baumel et al. 2018). Generalizability. While the results of the analyses in this dissertation are contextually bound to a certain set of users on TalkLife, I believe they can provide preliminary guidance for researchers interested in understanding peer support dynamics or the relationship between self-injury and online activity on similar platforms. Future research should seek to confirm the generalizability of these findings both in other samples on TalkLife and in other online peer communities. Some research suggests that individuals with chronic illnesses use different social platforms at different stages of their progression with illness (Sannon, Murnane, Bazarova, & Gay, 2019). It may similarly be the case that predictors discussed in this dissertation on TalkLife will be more or less predictive on other sites. A more comprehensive 195 analysis of the online social landscape will be critical to understand the trajectory of use among individuals at different stages of recovery (or at different points in readiness to change). Conclusion Self-injury represents a significant public health concern among young people (Wester, Trepel, & King, 2018; Swannell, Martin, Page, Hasking, & St. John, 2014) and online communities and mobile applications show promise in meeting individuals’ needs for support and resources. This dissertation provides a detailed description and empirical investigation of how a mobile peer support application, TalkLife, is used to discuss and exchange support for self-injury. Several gaps in research were addressed, however many areas for further study remain. Some of these include (1) establishing longitudinal evidence for the relationship between online activity and mental health and help-seeking outcomes, (2) understanding critical mechanisms leading to or preventing behavior change, and (3) empirically validating the impact of moderation and designing appropriate interventions. Global discussions around the responsibilities of platforms to intervene when graphic self-injury or suicidal content is published, demonstrate the imminent need for research to understand the effects of various online activities and effective moderation of these activities (Wright & Javid, 2019; Carman, 2019). Strong interdisciplinary collaborations between technologists, clinicians, and social scientists will be critical in advancing our understanding of how online peer support platforms can be used to help individuals with self-injury and other stigmatized conditions. 196 Appendix A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 6e+06 Model ● EEE ● EEI 5e+06 ● VVI ● VVV ● ● 4e+06 ● ● ● ● 1 2 3 4 5 6 7 8 9 Profiles Comparing model parameterizations for latent profile analysis 197 BIC (smaller value is better) Appendix B 750 key 500 BeFollowedCount CreatedAnswerCount CreatedQuestionCount FollowingCount reactions 250 0 Profile 1 (n = 54115) Profile 2 (n = 40143) Profile 3 (n = 11246) Categories associated with posts 198 mean Appendix C Most common Unigrams in dataset 199 Appendix D Most common Bigrams in dataset 200 Appendix E Categories Associated With Posts Self Harm Others Relationships Mental Health My Story Friends Hopes Family Health Positive Bullying Helpful tips LGBT Eating Disorders Education Music Work Poetry Religion Parenting Pregnancy Fears 0 50000 100000 150000 Number of posts 201 Categories Appendix F Moods Associated With Posts Afraid Angry Annoyed Anxious Embarrassed Exhausted Frustrated Furious Heartbroken Insecure Irritated Jealous Lonely Nervous Sad Sick Stressed Worried Mood valence Amazed Amused Astonished Ambiguous Caring Encouraged Negative Excited Happy Positive Inspired Loving Playful Positive Proud Relaxed Relieved Supportive Surprised Calm Chilled Confused Hungry Meh Numb Tired Shocked 0 25000 50000 75000 Number of posts 202 Moods Appendix G 1. In the past week, did you have thoughts of purposely hurting yourself without wanting to die? a. Yes b. No c. I don’t know d. I’d like to skip this question 2. In the past week, did you purposely hurt yourself without wanting to die? a. Yes b. No c. I don’t know d. I’d like to skip this question 3. How old were you the first time you purposefully hurt yourself without wanting to die? ____ 4. Overall, in the past week, how strong has your intention to purposely hurt yourself without wanting to die been? a. 0 – not at all strong b. 1 c. 2—somewhat strong d. 3 e. 4—very strong 5. Overall, in the past week, how strong has your ability to resist the urge to purposely hurt yourself without wanting to die been? a. 0 – not at all strong b. 1 c. 2—somewhat strong d. 3 e. 4—very strong 6. Have you received talk therapy? a. Yes b. No 7. How old are you? ____ 8. What is your gender? a. Female b. Male 203 c. Transgender (male to female) d. Transgender (female to male) e. Do not identify as male or female f. Declined to state g. Not sure 9. What is your race? a. White b. Black or African American c. Asian (for example, Chinese, Filipino, Indian) d. American Indian or Alaskan Native e. Native Hawaiian or other Pacific Islander f. Other 204 Appendix H Models predicting self-injury behavior (n =265, 632) M1 (full model) M2 (p < .10 included) M3 (p < .05 included) B SE OR B SE OR B SE OR Intercept -0.10 0.81 0.90 -0.27 0.71 0.77 -0.07 0.64 0.93 Covariates Age (13 – 17) (18 – 24) -1.46* 0.59 0.23 -1.43* 0.58 0.24 -1.44** 0.54 0.24 (25 – 38) -2.51** 0.83 0.08 -2.58** 0.82 0.08 -2.42** 0.75 0.09 Gender (Male) Female 1.11* 0.52 3.03 0.98† 0.51 2.66 0.93† 0.48 2.53 Trans / other 0.99 0.73 2.69 0.87 0.72 2.39 0.76 0.68 2.13 Race (White) Black -0.30 1.01 0.74 -0.30 1.01 0.74 -0.25 0.95 0.78 Asian -0.82 0.66 0.44 -0.76 0.65 0.47 -0.68 0.61 0.51 Other -1.95** 0.65 0.14 -1.94** 0.65 0.14 -1.46* 0.58 0.23 Time -0.17 0.19 0.84 -0.22 0.14 0.80 -0.22 0.13 0.80 Log Data Activity -0.32 0.39 0.73 Trigger posts 2.08 1.30 7.99 1.70* 0.78 5.47 1.68* 0.74 5.37 Trigger dismiss 0.80 2.63 2.22 Language I 0.36* 0.16 1.43 0.20* 0.08 1.22 We -3.48 2.35 0.03 Affiliation 0.35 0.61 1.42 Friend -1.80* 0.88 0.17 -1.42* 0.69 0.24 Family -0.18 0.93 0.84 Positive emo -0.05 0.18 0.95 Sad -1.07† 0.57 0.34 -0.73† 0.42 0.48 Anger -0.70 0.45 0.50 Rumination 0.44 0.35 1.55 Efficacy -0.31 0.25 0.73 Change Activity -0.01 0.02 0.99 Trigger posts 0.18 0.21 1.19 Trigger dismiss -0.19 0.12 0.82 -0.20* 0.10 0.82 -0.20* 0.10 0.81 Positive emo 0.10 0.11 1.11 Sad -0.34 0.39 0.71 Anger 0.13 0.26 1.14 Rumination -0.03 0.23 0.97 Variance Activity 0.05 0.04 1.05 Trigger posts -0.38 0.47 0.68 Trigger dismiss -0.03 0.24 0.97 Positive emo -0.29 0.20 0.75 -0.37* 0.18 0.69 Sad 1.02† 0.56 2.77 1.02** 0.37 2.77 Anger 0.00 0.34 1.00 Rumination -0.03 0.10 0.97 Deviance 645.2 657.9 674.6 Notes. † p < .10 * p < .05 ** p < .01 *** p < .001 205 Appendix I Models predicting self-injury thoughts (n = 266, 636) M1 (full model) M2 (p < .10 included) M3 (p < .05 included) B SE OR B SE OR B SE OR Intercept 2.09** 0.75 8.10 1.92*** 0.68 6.81 1.78** 0.67 5.91 Covariates Age (13 – 17) (18 – 24) -0.33 0.50 0.72 -0.35 0.48 0.71 -0.42 0.48 0.66 (25 – 38) -0.94 0.65 0.39 -1.08† 0.63 0.34 -1.18† 0.63 0.31 Gender (Male) Female 0.67 0.43 1.95 0.73† 0.42 2.08 0.72† 0.42 2.05 Trans / other 0.31 0.63 1.36 0.27 0.62 1.31 0.28 0.62 1.32 Race (White) Black -0.79 0.83 0.45 -0.87 0.83 0.42 -0.83 0.82 0.44 Asian -1.43* 0.56 0.24 -1.45** 0.55 0.23 -1.38* 0.55 0.25 Other -0.71 0.50 0.49 -0.65 0.49 0.52 -0.61 0.49 0.55 Time 0.02 0.16 1.02 -0.02 0.12 0.98 -0.03 0.12 0.97 Log Data Activity -0.91** 0.35 0.40 -0.84** 0.27 0.43 -0.45* 0.18 0.64 Trigger posts 2.80† 1.60 16.50 2.98* 1.25 19.63 2.88* 1.22 17.87 Trigger dismiss 0.06 2.53 1.06 Language I -0.13 0.11 0.88 We -2.40 † 1.32 0.09 -1.53† 0.90 0.22 Affiliation 0.60 0.54 1.82 Friend -0.38 0.55 0.69 Family -0.79 0.75 0.45 Positive emo -0.07 0.23 0.93 Sad 0.25 0.44 1.28 Anger 0.69 0.38 1.98 Rumination -0.14 0.26 0.87 Efficacy 0.07 0.19 1.08 Change Activity -0.02 0.02 0.98 Trigger posts -1.14† 0.61 0.32 -1.32* 0.57 0.27 -1.22* 0.55 0.29 Trigger dismiss -0.03 0.13 0.97 Positive emo 0.10 0.10 1.11 Sad -0.31 0.37 0.73 Anger -0.17 0.27 0.84 Rumination 0.13 0.21 1.13 Variance Activity 0.07 0.04 1.08 0.07† 0.04 1.07 Trigger posts -0.12 0.56 0.89 Trigger dismiss 0.26 0.27 1.30 Positive emo -0.16 0.19 0.85 Sad -0.27 0.52 0.76 Anger 0.18 0.39 1.20 Rumination 0.20† 0.12 1.22 0.15* 0.06 1.17 0.14* 0.06 1.15 Deviance 645.9 661.2 667.2 Notes. † p < .10 * p < .05 ** p < .01 *** p < .001 206 Appendix J Models predicting ability to resist self-injury (267, 605) M1 (full model) M2 (p < .10 included) M3 (p < .05 included) B SE B SE B SE Intercept 2.13*** 0.28 2.10*** 0.24 2.10*** 0.24 Covariates Age (13 – 17) (18 – 24) 0.22 0.19 0.22 0.19 0.25 0.19 (25 – 38) 0.11 0.25 0.13 0.25 0.15 0.25 Gender (Male) Female -0.24 0.17 -0.25 0.17 -0.24 0.17 Trans / other -0.53* 0.25 -0.51* 0.24 -0.50* 0.24 Race (White) Black 0.02 0.35 0.06 0.35 0.03 0.35 Asian -0.53* 0.21 -0.53* 0.21 -0.54* 0.21 Other -0.18 0.20 -0.16 0.20 -0.17 0.20 Time 0.04 0.06 0.06 0.05 0.06 0.05 Log Data Activity 0.02 0.14 Trigger posts -0.45 0.49 Trigger dismiss 1.21 0.87 1.42* 0.66 1.39* 0.66 Language I -0.07† 0.04 -0.07* 0.03 -0.07* 0.03 We 0.51 0.47 0.67† 0.39 Affiliation 0.06 0.15 Friend -0.09 0.19 Family -0.14 0.29 Positive emo 0.03 0.06 Sad -0.05 0.14 Anger 0.04 0.12 Rumination 0.00 0.10 Efficacy 0.12 0.08 0.12* 0.06 0.14* 0.06 Change Activity -0.01 0.01 Trigger posts -0.02 0.08 Trigger dismiss 0.07 0.05 Positive emo 0.05 0.04 0.05* 0.02 0.05* 0.02 Sad 0.14 0.14 Anger 0.10 0.09 Rumination -0.09 0.08 Variance Activity 0.01 0.01 Trigger posts 0.23 0.21 Trigger dismiss -0.19* 0.08 -0.18* 0.08 -0.18* 0.08 Positive emo 0.00 0.06 Sad -0.12 0.19 Anger 0.16 0.12 0.19* 0.09 0.19* 0.09 Rumination 0.03 0.03 Deviance 2063.1 2071.6 2075.7 Residual Variance 1.35 1.31 1.32 Notes. † p < .10 * p < .05 ** p < .01 *** p < .001 207 Appendix K Models predicting intentions to self-injure (n = 267, 613) M1 (full model) M2 (p < .10 included) M3 (p < .05 included) B SE B SE B SE Intercept 2.25*** 0.29 2.43*** 0.26 2.43*** 0.26 Covariates Age (13 – 17) (18 – 24) -0.02 0.20 -0.04 0.20 -0.05 0.20 (25 – 38) -0.53* 0.27 -0.55* 0.26 -0.58* 0.26 Gender (Male) Female 0.25 0.18 0.25 0.18 0.25 0.18 Trans / other 0.08 0.26 0.13 0.26 0.13 0.26 Race (White) Black 0.06 0.37 0.02 0.37 0.03 0.37 Asian -0.53* 0.23 -0.52* 0.22 -0.51* 0.23 Other -0.24 0.21 -0.21 0.21 -0.20 0.21 Time 0.04 0.05 0.01 0.04 0.01 0.04 Log Data Activity -0.36** 0.13 -0.37*** 0.09 -0.37*** 0.09 Trigger posts 0.16 0.45 Trigger dismiss 0.79 0.81 1.51* 0.61 1.50* 0.61 Language I 0.04 0.04 We -0.86* 0.42 -0.74† 0.38 Affiliation 0.30* 0.14 0.21* 0.10 Friend -0.34* 0.17 -0.25† 0.14 Family -0.85* 0.26 -0.73** 0.23 -0.63** 0.22 Positive emo -0.04 0.05 Sad -0.15 0.13 Anger -0.01 0.11 Rumination 0.05 0.09 Efficacy -0.03 0.07 Change Activity 0.00 0.01 Trigger posts 0.03 0.07 Trigger dismiss -0.08* 0.04 -0.06* 0.03 -0.06* 0.03 Positive emo 0.02 0.03 Sad 0.02 0.12 Anger -0.10 0.09 Rumination 0.03 0.07 Variance Activity 0.00 0.01 Trigger posts 0.10 0.19 Trigger dismiss 0.10 0.07 Positive emo -0.01 0.05 Sad 0.27 0.17 Anger 0.07 0.11 Rumination -0.03 0.03 Deviance 2014.4 2030.5 2037.3 Residual Variance 0.98 0.97 .98 Notes. † p < .10 * p < .05 ** p < .01 *** p < .001 208 Appendix L Models predicting SI behavior frequency (n = 267, 673 observations) M1 (full model) M2 (p < .10 included) M3 (p < .05 included) B SE B SE B SE Intercept 0.56*** 0.31 0.53*** 0.15 0.59*** 0.15 Covariates Age (13 – 17) (18 – 24) -0.22† 0.19 -0.26* 0.12 -0.26* 0.12 (25 – 38) -0.41* 0.32 -0.45* 0.16 -0.44* 0.16 Gender (Male) Female 0.19† 0.21 0.19† 0.11 0.19† 0.11 Trans / other 0.22 0.28 0.21 0.16 0.21 0.16 Race (White) Black -0.03 0.38 -0.01 0.22 -0.02 0.22 Asian -0.17 0.29 -0.15 0.14 -0.15 0.14 Other -0.24† 0.28 -0.21† 0.13 -0.19 0.13 Time 0.00 0.06 -0.01 0.02 -0.01 0.02 Log Data Activity -0.06 0.15 Trigger posts 0.27 0.47 0.44** 0.15 0.45** 0.15 Trigger dismiss -0.10 0.99 Language I 0.05* 0.07 0.03** 0.01 0.02* 0.01 We -0.41† 1.22 Affiliation 0.08 0.32 Friend -0.19† 0.40 -0.14† 0.08 Family 0.11 0.51 Positive emo -0.01 0.15 Sad -0.12 0.23 0.10† 0.06 Anger -0.05 0.20 Rumination -0.02 0.09 Efficacy 0.00 0.12 Change Activity 0.01 0.01 Trigger posts 0.02 0.08 Trigger dismiss -0.05* 0.04 Positive emo -0.02 0.05 Sad -0.09 0.15 Anger -0.04 0.11 Rumination 0.08† 0.08 Variance Activity 0.00 0.01 Trigger posts 0.06 0.19 Trigger dismiss 0.04 0.06 Positive emo -0.03 0.11 Sad 0.16 0.19 Anger 0.03 0.14 Rumination -0.01 0.03 Deviance 1519.5 1542.9 1549.7 Residual Variance 0.35 0.34 0.35 Notes. † p < .10 * p < .05 ** p < .01 *** p < .001 209 References Abramova, O., Krasnova, H., Wagner, A., & Buxmann, P. 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