Predicting Depression via Social Media

TLDR

Major depression is a serious public health challenge affecting tens of millions annually, yet only a fraction receive adequate treatment. The study explores using social media to detect and diagnose major depressive disorder in individuals. We crowdsourced Twitter users diagnosed with depression, analyzed their posts over a year before onset for behavioral cues such as social engagement, emotion, language, ego network structure, and medication mentions, and built a statistical classifier to estimate depression risk prior to onset. Social media signals—decreased activity, increased negative affect, highly clustered egonetworks, heightened relational and medication concerns, and greater religious expression—effectively characterize depression onset, suggesting the approach could aid healthcare agencies and individuals in proactive mental health management.

Abstract

Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.