In any given year, depression affects more than 6% of the adult population in just the United States (some 16 million people), while in Singapore, about 7% of the workforce has a history of mental illness at last count.
New research from the University of Pennsylvania and Stony Brook University finds a possible algorithm that could scan social media and point to linguistic red flags of depression before a formal medical diagnosis has been made.
Published in the Proceedings of the National Academy of Sciences, analysing social media data by consenting users across the months leading up to a depression diagnosis, the researchers found their algorithm could accurately predict future depression.
Indicators of the condition included mentions of hostility and loneliness, words like “tears” and “feelings,” and use of more first-person pronouns like “I” and “me.”
“What people write in social media and online captures an aspect of life that’s very hard in medicine and research to access otherwise,” says H. Andrew Schwartz, senior paper author and a principal investigator of the World Well-Being Project (WWBP).
“It’s a dimension that’s relatively untapped compared to biophysical markers of disease. Considering conditions such as depression, anxiety, and PTSD, for example, you find more signals in the way people express themselves digitally.”
For six years, the WWBP, based in Penn’s Positive Psychology Center and Stony Brook’s Human Language Analysis Lab, has been studying how the words people use reflect inner feelings and contentedness. In 2014, Johannes Eichstaedt, WWBP founding research scientist, started to wonder whether it was possible for social media to predict mental health outcomes, particularly for depression.
Eichstaedt and Schwartz teamed with colleagues Robert J. Smith, Raina Merchant, David Asch, and Lyle Ungar from the Penn Medicine Center for Digital Health for this study.
Rather than recruit participants who self-reported depression, the researchers identified data from people consenting to share Facebook statuses and electronic medical-record information, and then analysed the statuses using machine-learning techniques to identify those with a depression diagnosis.
Nearly 1,200 people consented to provide both digital archives. To build the algorithm, Eichstaedt, Smith, and colleagues looked back at 524,292 Facebook updates.
“There’s a perception that using social media is not good for one’s mental health,” Schwartz says, “but it may turn out to be an important tool for diagnosing, monitoring, and eventually treating it. Here, we’ve shown that it can be used with clinical records, a step toward improving mental health with social media.”
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