# What Twitter Profile and Posted Images Reveal About Depression and Anxiety

arXiv: https://arxiv.org/abs/1904.02670 · HTML: https://mlsanity.com/paper/1904.02670/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 17 of 17)

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- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
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- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [Computational Emotion Analysis From Images: Recent Advances and Future Directions](https://mlsanity.com/paper/2103.10798/) (2021)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text](https://mlsanity.com/paper/2209.05286/) (2022)
- [Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms](https://mlsanity.com/paper/2301.06815/) (2023)
- [Multi Class Depression Detection Through Tweets using Artificial Intelligence](https://mlsanity.com/paper/2404.13104/) (2024)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Predicting mental health using social media: A roadmap for future development](https://mlsanity.com/paper/2301.10453/) (2023)
- [Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination](https://mlsanity.com/paper/2401.05254/) (2024)
- [Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research](https://mlsanity.com/paper/2410.03545/) (2024)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter](https://mlsanity.com/paper/2207.14535/) (2022)
- [What Are You Anxious About? Examining Subjects of Anxiety during the COVID-19 Pandemic](https://mlsanity.com/paper/2209.13595/) (2022)
- [Discrete Prompt Tuning via Recursive Utilization of Black-box Multimodal Large Language Model for Personalized Visual Emotion Recognition](https://mlsanity.com/paper/2509.04480/) (2025)

## Abstract (arXiv)

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.