Penn Medicine researchers predict depression diagnoses from Facebook posts

A Facebook user's language ticks may tip off a clinician to their likelihood of developing depression, according to a recent study published in the Proceedings of the National Academy of Sciences.

Researchers from Philadelphia-based Penn Medicine and Stony Brook (N.Y.) University parsed through the Facebook statuses of more than 500,000 consenting patients, some of whom had been diagnosed with depression and some of whom had not, to determine what they called "depression-associated language markers."

Armed with these findings, the researchers created an algorithm they hoped would accurately predict a patient's likelihood of being diagnosed with depression, based only on language used in their Facebook statuses. The algorithm flagged Facebook statuses for language that referenced symptoms like sadness, loneliness and hostility, such as with words like "tears" or "feelings."

The algorithm also considered frequency of various words and phrases, such as how commonly a user posted a self-referential status with first-person pronouns like "I" and "me" — these users were likely to be diagnosed with depression.

"Social media data contain markers akin to the genome," Johannes Eichstaedt, PhD, founding research scientist of the World Well-Being Project at Penn and Stony Brook and a postdoctoral psychology fellow at the University of Pennsylvania, said in a statement. "Depression appears to be something quite detectable in this way; it really changes people's use of social media."

To evaluate the algorithm, the researchers applied it to the Facebook statuses of nearly 700 consenting patients who had visited an emergency department at a large urban academic medical center, 114 of whom had a diagnosis of depression listed in their medical records — and the algorithm proved accurate, even when only looking at a limited time frame.

In fact, the algorithm could accurately predict a patient's likelihood of being diagnosed with depression when only analyzing Facebook statuses from three months before the first documentation of depression in their medical record, according to the researchers.

Based on these findings, the researchers suggested social media networks, such as Facebook, could one day be used by clinicians to screen consenting patients for depression.

"The hope is that one day, these screening systems can be integrated into systems of care," Dr. Eichstaedt said. "This tool raises yellow flags; eventually the hope is that you could directly funnel people it identifies into scalable treatment modalities."

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