The things you post on social media can sometimes say a lot about you as a person, and even your mental state, and this is something that researchers are looking to explore more of.
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.
The system used a variety of factors to make its decision, such as choice of color, the filters used, face detection, user comments, and how much one is engaged in their posts. Depressed users also posted more photos with faces but had fewer people in their photos. That's more reliable, they say, than the 42 per cent success rate of family doctors trying to spot depression in their patients. In addition, the volunteers were asked to rate photos from the group of people without depression diagnoses - in this case, those users' most recent 100 photos.
Participants also agreed to disclose details of their mental health history, with around half saying they had suffered clinical depression within the last three years.
"This points toward a new method for early screening of depression and other emerging mental illnesses". It correctly identified people with depression 70 percent of the time, compared to general-practice doctors who had successful diagnoses about 40 percent of the time. The research team built a tool that analyzed the posts and identified depression through markers determined in previous research, such as the tendency of depressed people to prefer grayer, darker colors, and to show less evidence of social activity (which the researchers thought might be evidenced by the absence of faces in posted images).
"In other words, people suffering from depression were more likely to favor a filter that literally drained all the colour out the images they wanted to share", the scientists write.
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Another discovery was that healthy individuals tended to choose Instagram filters such as Valencia that give photos a warmer, brighter tone.
Dr Andrew Reece said: "Although we had a relatively small sample size, we were able to reliably observe differences in features of social media posts between depressed and non-depressed individuals".
Study co-leader Professor Chris Danforth, from the University of Vermont, US, said: "This study is not yet a diagnostic test, not by a long shot, but it is a proof of concept of a new way to help people".
In comparison, Global Positioning System have an average success rate for correctly diagnosing depression of 42%.
Chloe Grass-Orkin, from the charity Rethink Mental Illness, said the study offered could offer new prospects to identify people in need of support.