Mental Health Analysis via Social Media Data

With ubiquity of social media platforms, millions of people are routinely sharing their moods, feelings and even their daily struggles with mental health issues by expressing it verbally or indirectly through images they post. In this study, we aim to examine exploitation of big multi-modal social media data for studying depressive behavior and its population trend across the U.S. to better understand a regions influence on the prevailing environment and available care. In partic-ular, employing statistical techniques along with the fusion of heterogeneous features gleaned from different modalities (shared images and textual content), we build models to detect depressed individuals and their demographics.