Facebook posts may help predict depression risk: Study

The language people use in their Facebook posts can predict the future risk of depression as accurately as the tools clinicians use in medical settings to screen for the disorder, according to a study.

Analysing social media data shared by consenting users across the months leading up to a depression diagnosis, researchers from the University of Pennsylvania and Stony Brook University in the US developed an algorithm that 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," researchers said.

"What people write in social media and online captures an aspect of life that's very hard in medicine and research to access otherwise," said H Andrew Schwartz, principal investigator of the study published in the journal Proceedings of the National Academy of Sciences.

"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," Schwartz said.

The researchers identified data from people consenting to share Facebook statuses and electronic medical-record information.

They analysed the statuses using machine-learning techniques to distinguish those with a formal depression diagnosis.

Nearly 1,200 people consented to provide both digital archives. Of these, just 114 people had a diagnosis of depression in their medical records.

The researchers then matched every person with a diagnosis of depression with five who did not have such a diagnosis, to act as a control, for a total sample of 683 people.

The idea was to create as realistic a scenario as possible to train and test the algorithm, researchers said.

To build the algorithm, researchers looked back at 524,292 Facebook updates from the years leading to diagnosis for each individual with depression and for the same time span for the control.

They determined the most frequently used words and phrases and then modelled 200 topics to analyse what they called "depression-associated language markers."