Language in Facebook posts may help identify conditions such as diabetes, anxiety, depression, and psychosis in patients, according to a study from Penn Medicine and Stony Brook University researchers. It’s believed that language in posts could be indicators of disease and, with patient consent, could be monitored just like physical symptoms. This study was published in PLOS ONE.
“This work is early, but our hope is that the insights gleaned from these posts could be used to better inform patients and providers about their health,” says lead author Raina Merchant, the director of Penn Medicine’s Center for Digital Health and an associate professor of Emergency Medicine. “As social media posts are often about someone’s lifestyle choices and experiences or how they’re feeling, this information could provide additional information about disease management and exacerbation.”
Using an automated data collection technique, the researchers analyzed the entire Facebook post history of nearly 1,000 patients who agreed to have their electronic medical record data linked to their profiles. The researchers then built three models to analyze their predictive power for the patients: one model only analyzing the Facebook post language, another that used demographics such as age and sex, and the last that combined the two datasets.
Looking into 21 different conditions, researchers found that all 21 were predictable from Facebook alone. In fact, 10 of the conditions were better predicted through the use Facebook data instead of demographic information.
Read more at Penn Medicine News.
