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.
The work is still in very early stages, cautioned researchers from the University of Pennsylvania and Stony Brook University. But 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 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, they said.
"Depression is a really debilitating disease and we have treatments that can help people," said Raina Merchant, one of the study's 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.
Examining more than 500,000 Facebook posts from both groups, researchers determined which words, post lengths, frequency of posting and timing of posts were most associated with a depression diagnosis. They found people with depression used the words "I," "my" and "me" as well as such words as "hurt," "tired" and "hospital" more often than others in the months preceding their diagnoses.
Using indicators such as these, they built a computer model that could predict which people would receive a depression diagnosis with comparable accuracy to commonly used clinical surveys.
The model worked best when using Facebook data from the three months right before a participant received a depression diagnosis. When longer periods of Facebook data were included, the model became less precise.