Instagram Photos May Reveal Who Has Depression, Study Says
Instagram Photos May Reveal Who Has Depression, Study Says
Family doctors correctly diagnose depression 40 per cent of the time
09 August, 2017, 00:26
That's more reliable, they say, than the 42 per cent success rate of family doctors trying to spot depression in their patients. Those without depression "disproportionately favored the Valencia filter, which lightens the tint of photos". 71 of of the people who took part had a clinical diagnosis of depression.
As it turned out, depressed people were actually more likely to post photos with faces in them, but when healthy people did post images with faces, there tended to be more people in the photographs.
Depression is also characterized by reduced or avoidant social engagement.
Artificially intelligent computer software was used to identify depressed people from the colour content of their Instagram photos with an accuracy of 70%. They also found fewer images of faces on their Instagram feeds, which researchers likened to a key characteristic of depressed individuals who tend to socialize less.
If you post blue, dark and gray pictures on Instagram then this could be a sign that you are clinically depressed, a recently published study has revealed.
At the end of the study, researchers noted the photos' likes, the photographers' posting patterns, and the use of filters.
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"Pixel analysis of the photos in our dataset revealed that depressed individuals in our sample tended to post photos that were, on average, bluer, darker and greyer than those posted by healthy individuals", the researchers wrote in a blog post.
The researchers found that the volunteers could distinguish between users with depression diagnoses and those without the condition, to an extent.
Chris Danforth, who is also a co-director of the university's Computational Story Lab, commented that it is obvious for a person to have knowledge on a friend than computer; however, while flipping through the images, he might not be able to identify depression that efficiently.
"It's a proof of concept and for the particular individuals we studied, this set of predictors works for them". For example, the researchers said that they used a broad definition of depression, and looking at specific types of depression could lead to different results.
Danforth points out that while their research holds promise, the technology is still far from flawless.
Dr Christopher Danforth, study co-author said: "With an increasing share of our social interactions happening online, the potential for algorithmic identification of early-warning signs for a host of mental and physical illnesses is enormous".
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