# Understanding and Measuring Psychological Stress using Social Media

arXiv: https://arxiv.org/abs/1811.07430 · HTML: https://mlsanity.com/paper/1811.07430/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

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

- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations](https://mlsanity.com/paper/2405.16584/) (2024)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings](https://mlsanity.com/paper/2506.06616/) (2025)
- [Social Media Reveals Urban-Rural Differences in Stress across China](https://mlsanity.com/paper/2110.15726/) (2021)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [Cognition Chain for Explainable Psychological Stress Detection on Social Media](https://mlsanity.com/paper/2412.14009/) (2024)
- [MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation](https://mlsanity.com/paper/2505.03827/) (2025)
- [Quantifying the Suicidal Tendency on Social Media: A Survey](https://mlsanity.com/paper/2110.03663/) (2021)
- [Depression detection in social media posts using transformer-based models and auxiliary features](https://mlsanity.com/paper/2409.20048/) (2024)
- [Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models](https://mlsanity.com/paper/2202.01802/) (2022)
- [Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination](https://mlsanity.com/paper/2401.05254/) (2024)
- [Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm](https://mlsanity.com/paper/2502.13326/) (2025)
- [A Multimodal Framework for Depression Detection during Covid-19 via Harvesting Social Media: A Novel Dataset and Method](https://mlsanity.com/paper/2511.00424/) (2025)
- [The Healthy States of America: Creating a Health Taxonomy with Social Media](https://mlsanity.com/paper/2103.01169/) (2021)
- [Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion](https://mlsanity.com/paper/2208.13318/) (2022)
- [CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models](https://mlsanity.com/paper/2503.10707/) (2025)

## Abstract (arXiv)

A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social media-based measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.