Social media can provide a rich platform for those seeking better health and support through difficult experiences. Yet, it can also provide space for deviant mental health behaviors, very dangerous and stigmatized behaviors related to mental health. These behaviors are dangerous to participants in the communities as well as to platform health. However, the deep complexities of mental health and these clandestine behaviors resist straightforward, data-driven approaches to detection and intervention.
In this talk, I will describe how human-centered algorithms can identify and assess deviant mental health behaviors in online communities. This work combines methods from Machine Learning, Natural Language Processing, and data science with interdisciplinary insights from psychology and sociology. Using the case study of pro-eating disorder communities, I will show how human-centered insights to algorithms enable robust computational models that identify mental health signals in social media. Then, I will demonstrate how these algorithms can be used to understand latent impacts of these behaviors in online communities, such as content moderation and deviant behavior. I will conclude with discussing how human-centered insights can be brought to computational methods to answer our toughest questions about deviant behavior online.
Stevie Chancellor is a PhD candidate in Human Centered Computing in Interactive Computing at Georgia Tech, and she is advised by Munmun De Choudhury. Her research interest lies in using computational approaches to understanding deviant behavior in online communities. Prior to GT, she received a BA from the University of Virginia and an MA from Georgetown University. Stevie’s work has won multiple Best Paper Honorable Mention awards at CHI and CSCW, premier venues in human computer interaction. Her work has been supported by a Snap Inc. Research Fellowship, the Georgia Tech Foley Scholars program, and has appeared in national publications such as Wired and Gizmodo.