  We developed computational models to predict the emergence of depression and
Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of
depression history were collected from 204 individuals (105 depressed, 99
healthy). We extracted predictive features measuring affect, linguistic style,
and context from participant tweets (N=279,951) and built models using these
features with supervised learning algorithms. Resulting models successfully
discriminated between depressed and healthy content, and compared favorably to
general practitioners' average success rates in diagnosing depression. Results
held even when the analysis was restricted to content posted before first
depression diagnosis. State-space temporal analysis suggests that onset of
depression may be detectable from Twitter data several months prior to
diagnosis. Predictive results were replicated with a separate sample of
individuals diagnosed with PTSD (174 users, 243,775 tweets). A state-space time
series model revealed indicators of PTSD almost immediately post-trauma, often
many months prior to clinical diagnosis. These methods suggest a data-driven,
predictive approach for early screening and detection of mental illness.
