Predicting Depression via Social Media

🤖 Plain-English Summary

Major depression constitutes a serious challenge in personal and public health. 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.

🔑 Key Findings

• 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.

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