Computers Can Sense Depression Clues In Your Instagram Pics


The research was done in two stages: the first was about identifying the clues on Instagram photos that suggests the user might be depressed, while the second stage involved teaching the computer to detect those people using machine learning algorithms.

According to the study, this is better than the average doctor who correctly diagnoses depression 50 percent of the time. "Importantly, we also demonstrate that the markers of depression can be observed in posts made prior to the person receiving a clinical diagnosis of depression". Whether it's drive a vehicle, improve music discovery, retouch photos or narrate the world to blind people. The study was designed so that about half of the participants reported having been clinically depressed in the last three years. Ideas from well-established psychology studies on color, brightness and shading preferences of people were used for analyzing the images.

The team found that people with depression did indeed post photos that were, on average, bluer, darker, and grayer.

The healthy volunteers, on the other hand, chose filters that gave their photos a warmer, sunnier tone, such as Valencia. They also tended to have more photos with a single person in them, while non-depressed people shared more group shots.

"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.

Indications of depression were also provided by the faces in the images.

"Photos posted by people diagnosed with depression tended to be darker in color, received more comments from the community, were more likely to contain faces and less likely to have a filter applied".

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Danforth and Reece noted: "Fewer faces may be an oblique indicator that depressed users interact in smaller settings".

They added that the hypothesis of "sad-selfie" remains untested.

A study of Instagram pictures was able to diagnose depression in the app's users who had not been previously diagnosed, according to a published report.

They were able to up to a point, but not as effectively as the computer software.

And using these factors, they were able to nearly double the likelihood of correctly diagnosing a patient with the technology, compared to a doctor in a consultation. "But it is a proof of concept of a new way to help people". Or, imagine that you can go to the doctor and push a button to let an algorithm read your social media history as part of the exam'.

"We have a lot of thinking to do about the morality of machines", he said. "So much is encoded in our digital footprint". While considering issues like mental illness, clever artificial intelligence might be able to find signals.

He said it could potentially be used as part of screening for early signs of mental illness or help to confirm a diagnosis of depression.