Instagram photos reveal predictive markers of depression
By Andrew G. Reece, Christopher M. Danforth. EPJ Data Science, 2016
481 citations recorded by the source corpus.
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.
Subfields: Applied Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/36fe514cac2b805d0e5cbcec9c1426981955f397
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Psychology
In Social Psychology
- Christopher M. Danforth
- Andrew G. Reece
- Stevie Chancellor
- Andrew J. Reagan
- Peter Sheridan Dodds
- Ellen J. Langer
- Katharina Lix
- M. de Choudhury
Citations in the corpus
- Methods in predictive techniques for mental health status on social media: a critical review (2020, background): “Current examples would be Facebook, Twitter, Reddit, and Tumblr.”
- Forecasting the onset and course of mental illness with Twitter data (2016, methodology): “The methods used in recruitment, data collection, and analysis are adopted from Reece and Danforth12.”
- Detecting Narcissistic Personality Disorder Traits on Forums: Proof-of-Concept Study (2026, unknown): “Building on Pennebaker et al’s [24] demonstration that lexical markers reveal psychological states and Reece and Danforth’s [25] success in predicting depression from Instagram embeddings, our study aims to develop and rigorously evaluate embedding-based classifiers for detecting narcissistic…”