That's more reliable, they say, than the 42 per cent success rate of family doctors trying to spot depression in their patients.
The differences between mentally healthy and depressed people were tracked down to the filters on Instagram. A little less than half of the participants had been diagnosed with depression within the past three years.
Even the way depressed and healthy people chose to present their photos on Instagram was different.
Indeed, some of the photo features that the researchers identified "match common perceptions regarding the effects of depression on behavior", the authors noted. Analyzing factors such as hue, the use of filters and the presence of people, researchers were able to determine what they call "depression markers".
Co-authors Chris Danforth, a University of Vermont professor of mathematics and statistics, and Andrew Reece, a Harvard University graduate student in psychology, found that the Instagram photos of participants suffering from depression were "on average, bluer, darker and grayer than those posted by healthy individuals".
They did not take into consideration captions or comments, however, by examining the pixel of each picture, they were able to find out that pictures of a darker, bluer or grayer color were signs of depression.
The researchers were then able to predict which photos showed signs of depression using the study mode.
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The program wound up accurately assessing depression in Instagram users 70 percent of the time, the findings showed. This algorithm does not even need any kind of clinical diagnosis before confirmation.
We all know that social media can have a negative effect on our mental health and self esteem - seeing endless ideal Instagram snaps of slim models with tanned limbs in exotic locations can often lead anxiety, self-worth and self-confidence issues.
The research was done in two stages: the first was about identifying the clues on Instagram photos that suggests the user might be depressed, while the second stage involved teaching the computer to detect those people using machine learning algorithms.
Danforth points out that while their research holds promise, the technology is still far from flawless.
"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.
"This study is not yet a diagnostic test, not by a long shot", Danforth said in a statement, "but it is a proof of concept of a new way to help people".
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.
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