All these may seem like easy patterns to learn, but when the authors had volunteers look at the same photographs, their capacity to identify who had depression, while better than chance, was not as good as the computer. Indeed, to the extent other people could recognize depression from the photographs, it seems they may be using different cues from the machine.
One aspect of the study that runs against intuition is that there were more comments on posts made by people with depression than those without. Danforth told IFLScience this made only a small contribution to the capacity to identify depression compared to other factors; “So I wouldn’t put much weight behind that finding.” Nevertheless, it raises interesting questions if verified, possibly suggesting friends and family who are aware of someone's depression use increased commentary as a way of showing support.
Danforth imagines a time when “You can go to doctor and push a button to let an algorithm read your social media history as part of the exam." Alternatively, “Imagine an app you can install on your phone that pings your doctor for a checkup when your behavior changes for the worse, potentially before you even realize there is a problem."
Such a process seems desirable, compared to the low reliability of general practitioners, possibly because their assessments are based on narrow windows of time. On the other hand, it’s not hard to imagine such assessments being done without people’s permission, particularly if the test was extended to other forms of mental illness – big brother really could be watching you.
Neither scenario is imminent. “This study is not yet a diagnostic test, not by a long shot,” Danforth said. “We acknowledge that depression describes a general clinical status, and is frequently combined with other conditions,” the paper notes. Moreover, the algorithm did a substantially better job of recognizing depression when it looked at photographs taken both before and after a diagnosis had been made than when restricted to those taken beforehand, suggesting some behavior may be reinforced by diagnosis.