Mental health computing via harvesting social media data

Abstract

Mental health has become a general concern of people nowadays. It is of vital importance to detect and manage mental health issues before they turn into severe problems. Traditional psychological interventions are reliable, but expensive and hysteretic. With the rapid development of social media, people are increasingly sharing their daily lives and interacting with friends online. Via harvesting social media data, we comprehensively study the detection of mental wellness, with two typical mental problems, stress and depression, as specific examples. Initializing with binary user-level detection, we expand our research towards multiple contexts, by considering the trigger and level of mental health problems, and involving different social media platforms of different cultures. We construct several benchmark real-world datasets for analysis and propose a series of multi-modal detection models, whose effectiveness are verified by extensive experiments. We also make in-depth analysis to reveal the underlying online behaviors regarding these mental health issues.