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Mental illnesses adversely affect a significant proportion of the populationworldwide. However, the methods traditionally used for estimating andcharacterizing the prevalence of mental health conditions are time-consumingand expensive. Consequently, best-available estimates concerning the prevalenceof mental health conditions are often years out of date. Automated approachesto supplement these survey methods with broad, aggregated information derivedfrom social media content provides a potential means for near real-timeestimates at scale. These may, in turn, provide grist for supporting,evaluating and iteratively improving upon public health programs andinterventions.  We propose a novel model for automated mental health status quantificationthat incorporates user embeddings. This builds upon recent work exploringrepresentation learning methods that induce embeddings by leveraging socialmedia post histories. Such embeddings capture latent characteristics ofindividuals (e.g., political leanings) and encode a soft notion of homophily.In this paper, we investigate whether user embeddings learned from twitter posthistories encode information that correlates with mental health statuses. Tothis end, we estimated user embeddings for a set of users known to be affectedby depression and post-traumatic stress disorder (PTSD), and for a set ofdemographically matched `control' users. We then evaluated these embeddingswith respect to: (i) their ability to capture homophilic relations with respectto mental health status; and (ii) the performance of downstream mental healthprediction models based on these features. Our experimental results demonstratethat the user embeddings capture similarities between users with respect tomental conditions, and are predictive of mental health.

Quantifying Mental Health from Social Media with Neural User Embeddings