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
For a new spin on Mount Rainier — and sweeping views — give fire lookouts a try
on July 11, 2018 at 7:00 am
Amber Casali’s splendid new book, “Hiking Washington’s Fire Lookouts,” reports that by 1942, when Congress ended funding for Depression-era Civilian ... But as fire-detection methods advanced (aerial ... […]
Depression among SA women linked to poverty
on July 5, 2018 at 1:49 am
This sets in motion a dangerous cycle for both mums and babies,” he said. He said several studies highlight the gap that still exists in the detection of depression in pregnant women and new mothers i... […]
High levels of depression among SA women linked to poverty – Health dept
on July 5, 2018 at 12:44 am
READ MORE: Male depression may cause infertility – study He said several studies highlight the gap that still exists in the detection of depression in pregnant women and new mothers in South Africa. J... […]
High levels of depression amongst SA women linked to poverty – Health Dept
on July 4, 2018 at 10:53 pm
This sets in motion a dangerous cycle for both moms and babies,” he said. He said several studies highlight the gap that still exists in the detection of depression in pregnant women and new mothers i... […]
SA women more at risk to pre, post birth depression
on July 4, 2018 at 11:06 am
Several studies highlight the gap that still exist in the detection of depression in pregnant women and new mothers in SA. A number of screening tools have been tested in the country, but most are tim... […]
Teenage depression: If a parent doesn't get treatment for a child, is that abuse?
on July 4, 2018 at 2:29 am
Lack of screening for depression is one part of the problem in children’s mental health, and efforts are underway to improve screening and detection for depression. Access to care is another problem, ... […]
Companies are working to track signs of depression using data from your phone or smartwatch — and Olympian Michael Phelps is on board
on July 1, 2018 at 9:08 pm
"Can you see depression, touch depression ... "Part of [addressing mental illness] is better detection, but that's not the whole play here. We really need to think about how we intervene — how we pree... […]
Teen depression increases risk of violence: Need for early detection and intervention
on August 2, 2017 at 6:10 pm
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 longitud... […]
NIMHANS conducts workshop to help early detection of depression among youngsters
on January 28, 2017 at 8:27 pm
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. ... […]
Early detection for poor mental health a must
on September 26, 2016 at 8:29 pm
KUALA LUMPUR: Early detection of poor mental health and early treatment are important to prevent ... “Globally, one in five adults will suffer from one episode of depression in their lifetime and thos... […]
via Google News and Bing News