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According to a study, the language used in Facebook publications predicts the future risk of depression as accurately as the tools clinicians use in a medical setting to detect the disorder.
By analyzing social media data shared by consenting users in the months leading up to the diagnosis of depression, researchers from the University of Pennsylvania and Stony Brook University in the United States developed a algorithm for accurately predicting future depression.
Indicators of the condition included mentions of hostility and loneliness, words such as "tears" and "feelings," as well as the use of more first-person pronouns such as "I" and "me," said researchers.
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"What people write in social media and online represents an aspect of life that is very difficult to access in medicine and research," said H Andrew Schwartz, principal investigator of the 39, a study published in the journal Proceedings of the National Academy of Sciences.
"This is a relatively untapped dimension compared to the biophysical markers of the disease. By taking into account conditions such as depression, anxiety and PTSD, for example, you find more signals in the way people express themselves digitally, "said Schwartz.
The researchers identified data from people agreeing to share Facebook statuses and electronic medical record information.
They analyzed the states with the help of machine learning techniques to distinguish people diagnosed with formal depression.
Nearly 1,200 people agreed to provide both digital archives. Of these, only 114 people were diagnosed with depression in their medical records.
The researchers then compared each person diagnosed with depression to five other undiagnosed individuals, to serve as a control to 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 this algorithm, researchers looked at 524,292 Facebook updates of the years leading up to diagnosis for each person suffering from depression and during the same period for control.
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They determined the most frequently used words and phrases, and then modeled 200 subjects to analyze what they called "language markers associated with depression".
Finally, they compared how and how often the depressed and control participants used such phrasing.
The researchers learned that these markers included emotional, cognitive and interpersonal processes such as hostility and loneliness, sadness and rumination.
They could predict future depression three months before the first documentation of the disease in a medical record.
"There is a perception that using social media is not good for mental health, but it may prove to be an important tool for diagnosing, monitoring, and possibly treating," he said. Schwartz.
"Here we showed that it can be used with clinical records, a step forward to improve mental health through social media," he said.
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