Research. New research suggest that Instagram can be used for early screening and detection of mental illness. Using artificial intelligence, scientist have screened 43.950 Instagram photos and identified markers of depression.
“People in our sample who were depressed tended to post photos that, on a pixel-by-pixel basis, were bluer, darker and grayer on average than healthy people,” said Andrew Reece told The New York Times. He is a postdoctoral researcher at Harvard University and his co-author of the study is Christopher Danforth, a professor at the University of Vermont.
They found that depressed participants used fewer Instagram filters. When these users did add a filter, they tended to choose “Inkwell,” which drains a photo of its color, making it black-and-white. The healthier users tended to prefer “Valencia,” which lightens a photo’s tint.
Out of the hundreds of responses they received when asking for test persons at Amazon Mechanical Turk, they recruited a total of 166 people, 71 of whom had a history of depression.
The abstract of the research is this:
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.
