Here’s a phrase that psychiatrists will either love or hate: “State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis.”
Indeed, a team of researchers from the University of Vermont, Stanford, and Harvard found that depression and post-traumatic stress disorder (PTSD) can often be detected by applying machine learning to individuals’ Twitter feeds. The algorithms can also detect signs of the conditions long before a human doctor typically does.
Existing work on the topic informed the study’s predictive dimensions. The project examined affect, linguistic style, and context of hundreds of thousands of tweets from hundreds of users. Occurrence of negative terms like “death” and “never” as opposed to positive ones like “happy” factored into the analysis, as did tweet frequency, word count, and context.
The study recruited 105 patients with clinically diagnosed depression, alongside 99 healthy controls. The separate PTSD group featured 63 diagnosed participants and 111 controls. The high co-morbidity of the conditions allowed researchers to use similar predictors in both arms of the study, which was published last month in Scientific Reports.
– Ryan Black
Read more: Twitter Posts May Reveal Onset of Depression
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