publication . Preprint . Other literature type . Article . 2017

Instagram photos reveal predictive markers of depression

Andrew Reece; Christopher M. Danforth;
Open Access English
  • Published: 08 Aug 2017
Abstract
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners' average diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Photos posted by depressed individuals were more likely to be bluer, grayer, and darker. Human ratings of photo attributes (happy, sad, etc.) were weaker...
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doi: 10.1140/epjds/s13688-017-0110-z
Subjects
free text keywords: Computer Science - Social and Information Networks, Physics - Physics and Society, depression, psychology, machine learning, computational social science, Computer applications to medicine. Medical informatics, R858-859.7, Social media, Face detection, Cognitive psychology, Mental illness, medicine.disease, medicine, Uncorrelated, Metadata, Computer science, Computational sociology, Artificial intelligence, business.industry, business
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