"We’re increasingly understanding that what people do online is a form of behavior we can read with machine learning algorithms, the same way we can read any other kind of data in the world," says University of Pennsylvania psychologist Johannes Eichstaedt, first author of the new PNAS study and cofounder of the World Well-Being Project, a research organization investigating how the words people use on social media reflects their psychological state.

To study whether language on Facebook could predict a depression diagnosis, Eichstaedt and his colleagues needed access to two personal forms of data: Social media accounts and electronic medical records. Over the course of 26 months, they approached more than 11,000 patients in a Philadelphia emergency department and asked if they’d be willing to share their EMRs and up to seven years’ worth of Facebook status updates.

Some 1,200 patients agreed. Of those, 114 had medical records indicating a depression diagnosis. Every year, roughly one in six Americans suffers from depression. To reproduce that ratio in their final research population, the researchers matched every person with a depression diagnosis with five who did not. That gave the researchers a final pool of 684 participants. Using those individuals’ more-than-half-a-million Facebook status updates, the researchers determined the most frequently used words and phrases and developed an algorithm to spot what they call depression-associated language markers.

They found that people with depression used more "I" language (i.e. first-person singular pronouns) and words reflecting hostility and loneliness in the months preceding their clinical diagnosis. By training their algorithm to identify these language patterns, the researchers were able to predict future depression diagnoses as much as three months before its appearance in their medical records as a formal condition.

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