Depression affects more than 16 million Americans a year, but fewer than half get treatment. Now, researchers are turning to social media to shrink that gap and give doctors another way to find people at risk.

A study published in the Proceedings of the National Academy of Sciences suggests that analyzing language from Facebook posts can predict whether a user is depressed three months before the person receives a medical diagnosis.

The work is still in very early stages, the researchers cautioned. The study was based on a group of fewer than 700 users and the predictive model is only moderately accurate. But this approach could hold promise for the future, they said.

“Depression is a really debilitating disease and we have treatments that can help people,” said Raina Merchant, one of the authors and director of the Penn Medicine Center for Digital Health. “We want to think of new ways to get people resources and identification for depression earlier.”

Researchers recruited participants for the study from a hospital emergency department, asking for permission to access their electronic medical records and Facebook history.

For every participant who had a diagnosis of depression in the medical records, researchers found five people who did not — creating a sample that mirrored rates of depression in the national population.