Forecasting the onset and course of mental illness with Twitter data
By Andrew G. Reece, Andrew J. Reagan, Katharina Lix, Peter Sheridan Dodds, Christopher M. Danforth, Ellen J. Langer. Scientific Reports, 2016
375 citations recorded by the source corpus.
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, albeit in a separate population. 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 (Nusers = 174, Ntweets = 243,775). 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.
Subfields: Experimental and Cognitive Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/1290aa325025d705edc3b04d244eab2766779755
How researchers see this paper
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In Experimental and Cognitive Psychology
- Lyle Ungar
- Johannes C. Eichstaedt
- Margaret L. Kern
- Martin E. P. Seligman
- Sarah Mercer
- Benjamin Dreer
In Social Psychology
- Lyle Ungar
- Johannes C. Eichstaedt
- Margaret L. Kern
- David B. Yaden
- Martin E. P. Seligman
- Lucie Flekova
- Sarah Mercer
- Benjamin Dreer
Citations in the corpus
- Detecting depression and mental illness on social media: an integrative review (2017, background): “[11] predicted user depression and posttraumatic stress-disorder (PTSD) status from text and Twitter meta-data that preceded a reported first episode (see Figure 2 for examples of meta-data) with relatively high Areas under the Receiver Operating Characteristic (ROC) curve (AUCs) of .”
- Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “[22] found out that the first stage of depression may be detectable from Twitter data several months prior to its diagnosis with 0.”