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By Citizen Reporter


Facebook posts can give away your illnesses and conditions

Diabetes, anxiety, depression and psychosis can all be found by simple Facebook posts.

A study conducted by Penn Medicine and Stony Brook University has shown that in the case of some diseases your Facebook posts could be as good an indicator as most demographic data. The research published in Plos One has indicated that the language used in Facebook posts may help identify conditions such as diabetes, anxiety, depression and psychosis in patients.

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 21were predictable from Facebook alone. In fact, 10 of the conditions were better predicted through the use of Facebook data instead of demographic information.

“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,” said lead author Raina Merchant, the director of Penn Medicine’s Center for Digital Health and an associate professor of Emergency Medicine.

Some of the links between language and real-life conditions seemed obvious with “drink” and “bottle” clearly linked with instances of alcoholism. Others were however less easy to see, for instance, 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.

Our digital language captures powerful aspects of our lives that are likely quite different from what is captured through traditional medical data,” said the study’s senior author Andrew Schwartz, PhD, a visiting assistant professor at Penn in Computer and Information Science, and an assistant professor of Computer Science at Stony Brook University. “Many studies have now shown a link between language patterns and a specific disease, such as language predictive of depression or language that gives insights into whether someone is living with cancer. However, by looking across many medical conditions, we get a view of how conditions relate to each other, which can enable new applications of AI for medicine.”


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