The rate of detection by computers is 70% - a more dependable rate than the general practitioners' rate of successful diagnosis of depression at 42%. Those without depression "disproportionately favored the Valencia filter, which lightens the tint of photos".
"Depressed individuals in our study posted photos that were bluer, darker and grayer, compared to the posts of healthy participants", Reece said.
Depression is strongly associated with reduced social activity [20, 21]. "Our goal was to successfully identify and predict markers of depression in Instagram users' posted photographs", the researchers stated.
'In other words, people suffering from depression were more likely to favour a filter that literally drained all the colour out the images they wanted to share, ' said the scientists.
From studying the posting patterns of people suffering with mental health issues, they found that sharing lots of pictures on social media was an indicator of users that suffer with depression.
"Future research should seek to address the reliability of the computer model against more finely tuned definitions of depressive disorders, according to the researchers", said the team.
For the study, Chris Danforth, a professor at the University of Vermont's Department of Mathematics & Statistics, and Andrew Reece of Harvard University, asked 166 volunteers to share their Instagram feeds, along with details about their mental health history.
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"But you might not, as a person casually flipping through Instagram, be as good at detecting depression as you think". For example, previous research has suggested that depression is linked to a preference for darker, bluer and monochromatic colors. In a paper published today (Aug. 8) in the journal EPJ Data Science, a pair of researchers found that an algorithm flagging a few key signs in people's Instagram posts may be able to diagnose depression more effectively than doctors.
The scientists claim that artificial intelligence performed better, with Danforth saying: "Obviously you know your friends better than a computer". "But it is a proof of concept of a new way to help people".
In addition, much more research is needed before this type of technology could be used to diagnose mental health conditions.
He clarifies that it's "not yet a diagnostic test, not by a long shot".
"The end goal of this would be creating something that monitors a person's voice, how they're moving around and what their social network looks like ― all the stuff we already reveal to our phones", Danforth said.
We do feel strongly that there's an important ethical discussion that must occur in step with these technological developments, regarding data privacy and the implications of applying sophisticated analytical tools in an online medium which doesn't forget.