How to responsibly predict depression diagnoses using social media – Brookings Institution

Illustration of a master’s student in psychological distress, due to the health crisis, confinement, university closures, and social isolation. Maud Dupuy / Hans Lucas via Reuters Connect

In a year when the COVID-19 pandemic has wreaked so much havoc on the nation’s collective mental health, research has shown—unsurprisingly—that emotions like sadness, anxiety, depression, and stress are dramatically more prevalent now than they were this time last year. While those lamentable outcomes were measured through traditional surveys, a quiet revolution is under way in the underlying methodology of how mental health researchers and psychologists analyze the sentiment floating around our social media feeds and the internet more broadly.

By leveraging data from platforms like Twitter and Facebook, a growing number of doctors and academics (myself included) are using advanced textual analysis to determine what our choice of words reveals about ourselves in real-time. For instance, scholars have already demonstrated that it is possible to measure depression in an individual’s Facebook posts by detecting language predictors reflecting sadness, a preoccupation with the self, and expressions of loneliness and hostility.

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