When you’re feeling blue, your photos turn bluer, too. And more gray and dark as well, with fewer faces shown. In other words, just like people can signal their sadness by body language and behavior—think deep sighs and slumped shoulders—depression reveals itself in social media images.
That’s the conclusion of new research showing that computers, applying machine learning, can successfully detect depressed people from clues in their Instagram photos. The computer’s detection rate of 70 percent is more reliable than the 42 percent success rate of general-practice doctors diagnosing depression in-person.
“This points toward a new method for early screening of depression and other emerging mental illnesses,” says Chris Danforth, a professor at the University of Vermont who co-led the new study with Andrew Reece of Harvard University. “This algorithm can sometimes detect depression before a clinical diagnosis is made.”
Imagine that you can go to doctor and push a button to let an algorithm read your social media history as part of the exam. —Chris Danforth
The team’s results were published Aug. 8 in a leading data-science journal EPJ Data Science.
The scientists asked volunteers, recruited from Amazon’s Mechanical Turk, to share their Instagram feed as well as their mental health history. From 166 people, they collected 43,950 photos. The study was designed so that about half of the participants reported having been clinically depressed in the last three years.
Then they analyzed these photos, using insights from well-established psychology research, about people’s preferences for brightness, color, and shading. “Pixel analysis of the photos in our dataset revealed that depressed individuals in our sample tended to post photos that were, on average, bluer, darker, and grayer than those posted by healthy individuals,” Danforth and Reece write in a blog post to accompany their new study. They also found that healthy individuals chose Instagram filters, like Valencia, that gave their photos a warmer brighter tone. Among depressed people the most popular filter was Inkwell, making the photo black-and-white.
“In other words, people suffering from depression were more likely to favor a filter that literally drained all the color out the images they wanted to share,” the scientists write.
Faces in photos also turned out to provide signals about depression. The researchers found that depressed people were more likely than healthy people to post a photo with people’s faces—but these photos had fewer faces on average than the healthy people’s Instagram feeds. “Fewer faces may be an oblique indicator that depressed users interact in smaller settings,” Danforth and Reece note, which corresponds to other research linking depression to reduced social interaction—or it could be that depressed people take many self-portraits.
“This ‘sad-selfie’ hypothesis remains untested,” they write.
As part of the new study, Danforth and Reece had volunteers attempt to distinguish between Instagram posts made by depressed people versus healthy. They could, but not as effectively as the statistical computer model—and the human ratings had little or no correlation with the features of the photos detected by the computer. “Obviously you know your friends better than a computer,” says Chris Danforth, a professor in UVM’s Department of Mathematics & Statistics and co-director of the university’s Computational Story Lab, “but you might not, as a person casually flipping through Instagram, be as good at detecting depression as you think.”
Consider that more than half of a general practitioners’ depression diagnoses are false—a very expensive health care problem—while the computational algorithm did far better. The new study also shows that the computer model was able to detect signs of depression before a person’s date of diagnosis. “This could help you get to a doctor sooner,” Danforth says. “Or, imagine that you can go to doctor and push a button to let an algorithm read your social media history as part of the exam.”
As the world of machine learning and artificial intelligence expands into many areas of life, there are deep ethical questions and privacy concerns. “We have a lot of thinking to do about the morality of machines,” Danforth says. “So much is encoded in our digital footprint. Clever artificial intelligence will be able to find signals, especially for something like mental illness.” He thinks that this type of application may hold great promise for helping people early in the onset of mental illness, avoid false diagnoses, and offer a new lower-cost screening for mental health services, especially for those who might not otherwise have access to a trained expert, like a psychiatrist.
“This study is not yet a diagnostic test, not by a long shot,” says Danforth, “but it is a proof of concept of a new way to help people.”
Learn more: When You’re Blue, So Are Your Instagram Photos
The Latest on: Depression detection
- Patient Predictors of Detection of Depression and Anxiety Disorders in Primary Care on October 11, 2017 at 5:00 pm
In addition to patient disclosure, other patient factors in this study (being single, having more physical illnesses, the presence of psychiatric co-morbidity, more positive attitudes toward help-seeking) all contributed significantly to higher rates of ... […]
- Ivanka Trump reveals she struggled with postpartum depression on September 19, 2017 at 1:29 pm
First daughter Ivanka Trump has revealed that she struggled with postpartum depression ... depression is a medical problem and should be treated like one. "It's a very common disorder and we really don't give it the proper sort of detection and screening ... […]
- Teen depression increases risk of violence: Need for early detection and intervention on August 2, 2017 at 6:10 am
WASHINGTON D.C: There is a need for early detection and intervention of adolescent depression as a study reveals that teen depression increases the risk of violence. The research examined the longitudinal association between depression and subsequent ... […]
- NIMHANS conducts workshop to help early detection of depression among youngsters on January 28, 2017 at 8:27 am
Fourteen-year-old Vikas (name changed) had not been performing well at school. His younger sibling did well in academics and he felt he was "worthless" as he could not meet his parents' expectations. A chance remark to his father made the latter realise ... […]
- Back to black: why melancholia must be understood as distinct from depression on September 6, 2015 at 1:08 pm
First described by Hippocrates, “melancholia” or melancholic depression was considered a specific condition ... My research team is trying to establish melancholia’s categorical status and detection, and so improve its management. […]
- Early detection can stamp out depression on October 27, 2013 at 11:59 pm
It’s a scientific fact that people over 50 are more prone to cases of clinical depression. There are a number of proven reasons for it. You can take steps to eliminate those reasons and stop the “black beast” before it starts; or if you already have ... […]
- Early detection: Lebanon schools to screen seventh-graders for depression on February 17, 2013 at 1:45 am
LEBANON — In the months before she took her life, 13-year-old Jordyn Beckner had chances to reach out for help. It was toward the end of her seventh-grade year, and her mother, Jody Bledsoe, could tell the Lebanon teen was hiding some inner pain, even ... […]
- New Parents at Risk for Depression on September 8, 2010 at 9:11 am
“The U.K. National Institute for Health and Clinical Excellence recommends routine screening for depression among postnatal mothers; however, no such policy exists for fathers,” the study authors wrote. “There is a need for appropriate detection of ... […]
- Preschool depression: The importance of early detection of depression in young children on May 19, 2010 at 7:50 am
It is difficult to imagine a depressed third-grader. It is even more difficult to imagine a depressed preschooler. Although childhood depression is a well-recognized and treated disorder, only recently have research studies begun looking at depression in ... […]
via Google News and Bing News