Document Type

Article

Publication Date

12-1-2017

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 unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.

Comments

Erratum to: Instagram photos reveal predictive markers of depression (EPJ Data Science, (2017), 6, 1, (15), 10.1140/epjds/s13688-017-0110-z)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights Information

© 2017, The Author(s).

DOI

10.1140/epjds/s13688-017-0110-z

Link to Article at Publisher Website

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