Depressed participants were more likely to post photos containing a face. But when healthier participants did post photos with faces, theirs tended to feature more of them, on average.
As revealing as the findings are about Instagram posts specifically, both Mr. Reece and Mr. Danforth said the results speak more to the promise of their techniques.
“This is only a few hundred people, and they’re pretty special,” Mr. Danforth said of the study participants. “There’s a sieve we sent them through.”
To be included in the study, participants had to meet several criteria. They had to be active and highly rated on Amazon’s Mechanical Turk platform, a paid crowdsourcing platform that researchers often use to find participants. They also had to be active on Instagram and willing to share their entire posting history with the researchers. Finally, they had to share whether or not they had received a clinical diagnosis of depression.
Out of the hundreds of responses they received, Mr. Reece and Mr. Danforth recruited a total of 166 people, 71 of whom had a history of depression. They collected nearly 44,000 photos in all.
The researchers then used software to analyze each photo’s hue, color saturation and brightness, as well as the number of faces it contained. They also collected information about the number of posts per user and the number of comments and likes on each post.
Using machine-learning tools, Mr. Reece and Mr. Danforth found that the more comments a post received, the more likely it was to have been posted by a depressed participant. The opposite was true for likes. And depressed users tended to post more frequently, they found.
Though they warned that their findings may not apply to all Instagram users, Mr. Reece and Mr. Danforth argued that the results suggest that a similar machine-learning model could someday prove useful in conducting or augmenting mental health screenings.
“We reveal a great deal about our behavior with our activities,” Mr. Danforth said, “and we’re a lot more predictable than we’d like to think.”
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