Computational Methods in the Study of Individuals' Attention Online

Abstract

This dissertation uses computational methods to study individuals' attention online with the explicit goal of enabling information systems to support better use of people's attention. As consumption of information shifts to digital means, systems are playing a increasing role in shaping both the information we pay attention to and the practices for paying attention. Computer scientists are uniquely positioned to explore this unprecedented opportunity to design systems that impact millions of people, and support more efficient and effective use of human attention. However, incomplete measures of online attention and little research on the determinants of attention in online settings hamper the ability to design better information systems. To this end, this dissertation develops measures and methods to investigate individuals' attention online as it manifest in two of the most important domains of online activity: online news and social media. We devise new Web scale measures for capturing individuals' attention using non-invasive digital traces of online activity. In addition, we design novel computational methodology for studying the social, cognitive, and technological factors that affect attention online. Overall, this dissertation lays the foundation for assessing the impact information systems have on human attention, and provides guidelines for the design of better information systems in the future.