Human-centered algorithms and ethical practices to understand deviant mental health behaviors in online communities
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Social media has changed how individuals cope with health challenges in complex ways. In some mental health communities, individuals promote deliberate self-injury, disordered eating habits, and suicidal ideas as acceptable choices rather than dangerous actions. In particular, I study focuses on the pro-eating disorder (pro-ED) community, a clandestine group that advocates for eating disorders as lifestyle choices rather than a dangerous and potentially life-threatening mental illnesses. This thesis develops human-centered algorithmic approaches to understand these deviant and dangerous behaviors on social media. Using large-scale social media datasets and techniques like machine learning, computational linguistics, and statistical modeling, I analyze and understand patterns of behavior in pro-ED communities, how they interact with others on the platform, and these latent impacts. Through eight empirical examinations and an analytical essay, I demonstrate that computational approaches can identify pro-ED and related behaviors on social media as well as documenting larger-scale community and platform changes and interactions with dangerous content. I also consider the impacts that methods, ethics, and practices of conducting this work have on these communities. In sum, this thesis represents the beginnings of an interdisciplinary approach to problem-solving for complex, vulnerable communities on social media.