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Depression

Researchers are using social media to help find people at risk of depression. 

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 from the University of Pennsylvania and Stony Brook University 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,” says Raina Merchant, one of the study 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 study participants from a hospital emergency department, asking for permission to access their electronic medical records and Facebook history. For every participant with a diagnosis of depression, 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,” and “hurt, tired, and hospital,” more often than others in the months preceding their diagnosis. 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.

“We’re at the very beginning of trying to understand how this data is sometimes people just saying hi to each other, but sometimes it can give us insight into the health of individuals and communities,” Merchant says.

Depression can be difficult to diagnose. Most screening tools rely on people accurately reporting their own symptoms and answering survey questions, and primary care doctors can screen for depression, but their patient visits often are short and months apart.

“With social media and other data, you can start to fill in those gaps,” says Munmun De Choudhury, an assistant professor in Georgia Tech’s School of Interactive Computing.

In the future, if patients shared social media data with their doctors, it could create more personalized care, De Choudhury says. It also could be used for public health, too. For example, the Centers for Disease Control and Prevention could figure out which communities are most at risk for suicide by examining their online posts, and then target specific prevention measures to them.

Facebook and Google have started taking steps in this direction. Facebook uses artificial intelligence to flag posts that indicate risks of self harm or suicide. From there, employees can direct people to national suicide prevention resources. Google prompts users who search depression-related terms to take a screening questionnaire.

It’s encouraging to see these companies take social responsibility, De Choudhury says, but this can be only one aspect of mental health care. Predictive models built on social media are not highly accurate yet. They’re also built on small sample sizes, which means they may not work the same in a large, diverse population.

“You shouldn’t be using such an algorithm by itself at any point in time,” she says. It needs to be combined with traditional screening surveys for depression and clinical expertise.

There also is the issue of privacy. “We should see this data the way we do any health data,” Merchant says. But it’s a tricky premise, given recent high-profile data breaches.

There also are concerns that social media do more than reflect one’s mental health. Some studies have shown that those with greater social media use are more likely to be depressed or have eating disorders. But other studies show social media can be helpful in connecting people to resources and peer support.

More research is needed, Merchant says. “We need a better understanding of not just how it tells us about our health, but also how the use of technology affects our health.”

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