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.
Emotional filters
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.
Algorithmic aid
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
[google_news title=”” keyword=”depression detection” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
- Fatal ‘zombie deer’ disease found in Harpers Ferry National Historical Parkon April 24, 2024 at 12:20 pm
Two white-tailed deer inside Harpers Ferry National Historical Park in West Virginia have tested positive for the fatal Chronic Wasting Disease.
- Study suggests that living near green spaces reduces the risk of depression and anxietyon April 23, 2024 at 6:50 am
Over the past decades, a growing number of people have migrated to urban areas, while the size and population of rural areas have drastically declined. While parks and other green spaces are often ...
- Early Detection of Multiple Sclerosis Years Before Symptoms Using Blood Testson April 23, 2024 at 1:40 am
UCSF researchers discover blood autoantibodies predicting MS before symptoms, enabling early intervention for better outcomes.
- TSA explosives detection canine given party to celebrate retirementon April 19, 2024 at 10:36 am
Messi, the TSA explosives detection canine, had a retirement party in Ronald Reagan Washington National Airport complete with over a dozen tennis balls, a bubble machine, party hat and cupcake.
- The fear of depression recurrence is potent but not universal: Concordia researchon April 19, 2024 at 7:15 am
Clinicians treating patients who live with or survive serious diseases such as cancer are familiar with the concept of fear of illness recurrence (FIR). FIR has been associated with greater ...
- Depression in College Studentson April 18, 2024 at 4:00 pm
Although not everyone who is depressed will have suicidal ideas, the prevalence of suicidal ideation is quite high.
- Patient Predictors of Detection of Depression and Anxiety Disorders in Primary Careon April 17, 2024 at 5:00 pm
Depression and anxiety disorders were selected ... past 12 months (not including the current visit). Correct physician detection was determined by agreement between the medical chart of the ...
- Study: Depression is largely prevalent, but undiagnosed in SLEon April 15, 2024 at 9:41 am
Among 40 people with lupus surveryed in Pakistan, all but two showed symptoms of depression, which correlated with disease severity.
- Patient Predictors of Detection of Depression and Anxiety Disorders in Primary Careon April 14, 2024 at 5:00 pm
Background: The aim of the present study was to examine the contribution of various patient factors to the detection of anxiety and depression within primary care. Methods: This multicenter survey ...
- What is high functioning depression that Kusha Kapila talked about; symptoms that people misson April 12, 2024 at 5:30 am
Social media influencer turned actress, Kusha Kapila, bravely shared her journey with ADD and high-functioning depression during a 2020 interview. Her story sheds light on the challenges faced by ...
via Google News and Bing News