Instagram photos reveal predictive markers of depression

TLDR

The study extracted statistical features from 43,950 Instagram photos using color analysis, metadata, and face detection. Machine‑learning models identified depression markers from Instagram photos, outperforming general practitioners and remaining predictive before diagnosis, suggesting new avenues for early screening.

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.