Quantifying and Predicting Mental Illness Severity in Online Pro Eating Disorder Communities
Social media sites have struggled with the presence of emotional and physical self injury content. Individuals who share such content are often challenged with severe mental illnesses like eating disorders. We present the first study quantifying levels of mental illness severity (MIS) in social media. We examine a set of users on Instagram who post content on pro eating disorder tags (26M posts from 100K users) Our novel statistical methodology combines topic modeling and novice/clinician annotations to infer MIS in a user's content. Alarmingly, we find that proportion of users whose content expresses high MIS have been on the rise since 2012 (13%/year increase) Previous MIS in a user's content over seven months can predict future risk with 81% accuracy. Our model can also forecast MIS levels up to eight months in the future with performance better than baseline. We discuss the health outcomes and design implications as well as ethical considerations of this line of research.
CSCW