Understanding and Predicting Multiple Risky Behaviors from Social Media

Yiheng Zhou, Catherine Glenn, Jiebo Luo

Abstract: According to the World Bank, risky behaviors are increasingly widespread globally and pose a growing threat to individual health and society. Recently, a number of studies have been done to study risky behaviors, such as understanding illicit drug use behaviors using social media data, and predicting drinking behavior and alcohol-related problems among fraternity and sorority members. However, the majority of the related work only focuses on one risky behavior. Research in clinical psychology and public health domains tell us that there may exist some correlations among risk behaviors. In this paper, in order to support and utilize this correlation, we investigate five risky behaviors: drug consumption, drinking, sleep disorder, depression, and eating disorder. We utilize Instagram data to discover the correlation between those five risk behaviors and employ multi-task machine learning techniques to predict the potential risk behaviors in the near future for the Instagram users.