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dc.contributor.advisorDe Choudhury, Munmun
dc.contributor.authorChancellor, Stevie
dc.date.accessioned2019-08-21T13:54:43Z
dc.date.available2019-08-21T13:54:43Z
dc.date.created2019-08
dc.date.issued2019-07-19
dc.date.submittedAugust 2019
dc.identifier.urihttp://hdl.handle.net/1853/61778
dc.description.abstractSocial 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectSocial media
dc.subjectMachine learning
dc.subjectHuman-centered computing
dc.subjectDeviant behavior
dc.subjectOnline communities
dc.subjectData science
dc.titleHuman-centered algorithms and ethical practices to understand deviant mental health behaviors in online communities
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentInteractive Computing
thesis.degree.levelDoctoral
dc.contributor.committeeMemberBruckman, Amy
dc.contributor.committeeMemberGilbert, Eric
dc.contributor.committeeMemberPratt, Wanda
dc.contributor.committeeMemberCounts, Scott
dc.date.updated2019-08-21T13:54:43Z


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