Forecasting the onset and course of mental illness with Twitter data

Abstract

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.

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

“…Critically for this work, it also includes the time that a particular piece of language was authored by the user. Social media is, thus, one data source through which the early signs of mental illness and suicide can be detected (Reece et al, 2016;Bryan et al, in press). Quantifiable signals for a wide range of behavioral health conditions have been uncovered recently, and this provides a foothold into analysis and intervention empowered by data science.…”

“…used crowdsourcing, via the Amazon Mechanical Turk platform, to collect Twitter usernames as well as labels for depression. Reece and Danforth (2016) used a similar crowdsourcing approach to collect both depression labels and Instagram photo data. In some approaches to annotation, depression is subsumed into broader categories like distress, anxiety, or crisis.…”

Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram (Preprint)

“…Finally, we used an emoji sentiment scale, which maps emojis, Unicode-based emoticons, to a happiness score [20]. LabMT and ANEW have been previously used for predicting depression, while the use of an emoji sentiment is novel for this purpose [11].…”

“…Recent evidence shows significant predictive power to identify MDD, particularly in users of Twitter and Facebook [10][11][12][13]. However, the majority of research conducted to predict MDD in social media users has focused on user-generated content, i.e., the content created by the user themselves.…”

How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study (Preprint)

“…Internet data has been used to monitor health behaviors, for a variety of conditions ranging from infectious diseases [15]- [17] to mental health conditions [18]. Anonymous venues online, especially search engines [19] allow people to seek information on sensitive topics.…”