Instagram photos reveal predictive markers of depression

TL;DR

This study demonstrates that machine learning analysis of Instagram photos can effectively identify markers of depression, outperforming general practitioners and enabling early detection through computational image features.

Contribution

The paper introduces a novel approach using computational analysis of Instagram images to predict depression, surpassing traditional diagnostic methods.

Findings

Photos of depressed individuals are bluer, grayer, and darker.

Machine learning models outperform general practitioners in depression detection.

Computational features are better predictors than human-rated attributes.

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 predictors of depression, and were uncorrelated with computationally-generated features. These findings suggest new avenues for early screening and detection of mental illness.