Data Acquisition

Computer Program Identifies Depressed Persons from Social Media Images

08 August 2017

Researchers have developed a computer program that is capable of correctly identifying depressed persons based on their social media images alone.

The computer program, which is described in the journal EPJ Data Science, can identify people suffering from depression using their social media images with a 70 percent rate of accuracy, according to researchers.

Dr. Christopher Danforth, study co-author from the University of Vermont, said: "Our analysis of user accounts from a popular social media app revealed that 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. When they did select a filter they were more likely to use the filter that converted color images to black and white. People diagnosed with depression also posted at a higher frequency compared to non-depressed individuals."

Dr. Danforth added: "With an increasing share of our social interactions happening online, the potential for algorithmic identification of early-warning signs for a host of mental and physical illnesses is enormous. Imagine an app you can install on your phone that pings your doctor for a check-up when your behavior changes for the worse, potentially before you even realize there is a problem."

Evaluating over 43,000 social media photos supplied by 166 study participants (71 of whom had a clinical diagnosis of depression), the program identified details from the pictures—posted before participants were diagnosed with depression—thought to be closely linked with both depressed and non-depressed individuals. Using that information, the program made assumptions about which individuals would go on to be diagnosed with depression.

Dr. Andrew Reece, study co-author from Harvard University, said: "Although we had a relatively small sample size, we were able to reliably observe differences in features of social media posts between depressed and non-depressed individuals. 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."

Researchers will continue to test the program taking into consideration more specific definitions of depression apart from the generalized one used for purposes of this study.



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