Human-centered algorithms and ethical practices to understand deviant mental health behaviors in online communities

Abstract

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.