# Instagram photos reveal predictive markers of depression

arXiv: https://arxiv.org/abs/1608.03282 · HTML: https://mlsanity.com/paper/1608.03282/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 25 of 25)

- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media](https://mlsanity.com/paper/2305.05138/) (2023)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning](https://mlsanity.com/paper/2310.16538/) (2023)
- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models](https://mlsanity.com/paper/2409.14794/) (2024)
- [Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust](https://mlsanity.com/paper/2103.15909/) (2021)
- [Quantifying language changes surrounding mental health on Twitter](https://mlsanity.com/paper/2106.01481/) (2021)
- [Quantifying the Suicidal Tendency on Social Media: A Survey](https://mlsanity.com/paper/2110.03663/) (2021)
- [Pre-Trained Language Transformers are Universal Image Classifiers](https://mlsanity.com/paper/2201.10182/) (2022)
- [Predicting mental health using social media: A roadmap for future development](https://mlsanity.com/paper/2301.10453/) (2023)
- [Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy](https://mlsanity.com/paper/2104.14281/) (2021)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [AI agents for facilitating social interactions and wellbeing](https://mlsanity.com/paper/2203.06244/) (2022)
- [Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic](https://mlsanity.com/paper/2203.13770/) (2022)
- [Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives](https://mlsanity.com/paper/2507.09565/) (2025)
- [A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach](https://mlsanity.com/paper/2508.18301/) (2025)
- [Social Media for Mental Health: Data, Methods, and Findings](https://mlsanity.com/paper/2511.07914/) (2025)
- [MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features](https://mlsanity.com/paper/2511.15675/) (2025)
- [The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating](https://mlsanity.com/paper/2603.26727/) (2026)
- [A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring](https://mlsanity.com/paper/2605.14360/) (2026)
- [FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes](https://mlsanity.com/paper/2606.02523/) (2026)
- [VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection](https://mlsanity.com/paper/2609.05537/) (2026)

## Abstract (arXiv)

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.