Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

TLDR

Depression contributes heavily to global disease burden, yet most patients do not seek early clinical diagnosis, and social media increasingly offers a platform for detecting mental health conditions. This study seeks to enable timely depression detection by harvesting data from social media. We built a labeled Twitter dataset, extracted six feature groups spanning clinical criteria and online behaviors, and applied a multimodal dictionary‑learning model to identify depressed users. Experiments show the model outperforms baselines by 3–10 % and reveal distinct online behavior patterns distinguishing depressed from non‑depressed users.

Abstract

Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.