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 (N users = 174, N tweets = 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. Social media data provide valuable clues about physical and mental health conditions. This holds true even in cases where social media users are not yet aware that their health has changed. For example, searching for information on certain health symptoms has been shown to provide accurate early-warning indicators for hard-to-detect cancers 1 . Social media networks have been used to plot the trajectory of disease outbreaks [2] [3] [4] , and to track regional dietary health 5 . In addition to physical ailments, predictive screening methods have successfully identified markers in social media data for a number of mental health issues, including addiction 6 , depression [7] [8] [9] [10] [11] [12] , Post-Traumatic Stress Disorder (PTSD) 13, 14 , and suicidal ideation 15 . The field of predictive health screening with social media data is still in its infancy, however, and considerable refinements are needed to develop methodologies that can effectively augment health care. In this report, we present a set of improved methods and novel contributions for predicting and tracking depression and PTSD on Twitter. Depression has emerged as the leading mental health condition of interest among computational social scientists [7] [8] [9] [10] [11] [12] , as it is a relatively common mental disorder 16 and influences a range of behaviors and patterns of communication 17 . Underdiagnosis of depression remains a persistent problem; a recent survey of a major metropolitan area found nearly half (45%) of all cases of major depression were undiagnosed 18 . PTSD, while less common 19 , is frequently comorbid with major depression 20 . Studies have found that PTSD is underdiagnosed or under-treated by a majority of primary-care physicians 21, 22 . The costs of underdiagnosis of these conditions, both to human quality of life and health care systems, are considerable. Computational methods for early screening and diagnosis of depression and PTSD have the potential to make a positive impact on a major public health issue, with minimal associated costs and labor intensity. Improvements and novel contributions. Early efforts to detect depression and PTSD signals in Twitter data have been promising. Park et al. 11 established that Twitter users suffering from depression tended to post tweets containing more negative emotional sentiment compared to healthy users. De Choudhury et al. 7 successfully identified new mothers suffering from postpartum depression, based on changes in Twitter usage and tweet content. In a separate analysis, De Choudhury et al. 8 found that depressive signals were observable in tweets made
Reece et al. REF found out that the first stage of depression may be detectable from Twitter data several months prior to its diagnosis with 0.87 AUC of performance probability.
Forecasting the onset and course of mental illness with Twitter data
Scientific Reports
Scientific Reports
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