Modeling Personal Experiences Shared in Online Communities
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Written communications about personal experiences, such as giving birth or reading a book, can be both rhetorically powerful and statistically difficult to model. My research explores unsupervised natural language processing (NLP) models to represent complex personal experiences and self-disclosures communicated in online communities, while also re-examining these models for biases and instabilities. I seek to reliably represent individual experiences within their social contexts and model interpretive dimensions that illuminate both patterns and outliers, while addressing social and humanistic questions. Through this work, I develop a data science practice that emphasizes cross-disciplinary collaborations and care for datasets and their authors. In this dissertation, I share case studies that highlight both the opportunities and the risks in reusing NLP models for context-specific research questions.
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149 pages
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2022-08
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Mimno, David
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Rzeszotarski, Jeff
Lee, Lillian
Lee, Lillian
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Information Science
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Ph. D., Information Science
Degree Level
Doctor of Philosophy
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Government Document
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Attribution 4.0 International
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dissertation or thesis