Your Facebook posts may help identify conditions such as diabetes, anxiety, depression and psychosis, say scientists who suggests that language on social media posts with patient consent could be monitored just like physical symptoms.
Using an automated data collection technique, the researchers from University of Pennsylvania and Stony Brook University in the US analysed 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 analyse their predictive power for the patients: one model only analysing 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.
"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," said Raina Merchant, an associate professor at University of Pennsylvania.
"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," Merchant said.
Some of the Facebook data that was found to be more predictive than demographic data seemed intuitive.
For example, "drink" and "bottle" were shown to be more predictive of alcohol abuse. However, others were not as easy.
For example, the people that most often mentioned religious language like "God" or "pray" in their posts were 15 times more likely to have diabetes than those who used these terms the least.
Additionally, words expressing hostility — like "dumb" and some expletives — served as indicators of drug abuse and psychoses.
"Our digital language captures powerful aspects of our lives that are likely quite different from what is captured through traditional medical data," said Andrew Schwartz, an assistant professor at Stony Brook University.