# Robust language-based mental health assessments in time and space through social media

arXiv: https://arxiv.org/abs/2302.12952 · HTML: https://mlsanity.com/paper/2302.12952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 93 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions](https://mlsanity.com/paper/1808.09600/) (2018)
- [Correcting Sociodemographic Selection Biases for Population Prediction from Social Media](https://mlsanity.com/paper/1911.03855/) (2019)
- [Freshman or Fresher? Quantifying the Geographic Variation of Internet Language](https://mlsanity.com/paper/1510.06786/) (2015)
- [A survey of location inference techniques on Twitter](https://mlsanity.com/paper/1701.03639/) (2017)
- [The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place](https://mlsanity.com/paper/1302.3299/) (2013)
- [Happiness and the Patterns of Life: A Study of Geolocated Tweets](https://mlsanity.com/paper/1304.1296/) (2013)
- [World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews](https://mlsanity.com/paper/2011.06457/) (2020)

## Built on by (most cited first, 5 of 5)

- [Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts](https://mlsanity.com/paper/2407.13228/) (2024)
- [Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives](https://mlsanity.com/paper/2507.09565/) (2025)
- [Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety](https://mlsanity.com/paper/2508.21722/) (2025)
- [Linguistic trajectories of bipolar disorder on social media](https://mlsanity.com/paper/2509.10035/) (2025)
- [A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies](https://mlsanity.com/paper/2602.06015/) (2026)

## Abstract (arXiv)

Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the Centers for Disease Control or Gallup, mental health is only broadly captured through "mentally unhealthy days" or "sadness", and limited to relatively infrequent state or metropolitan estimates. Through the large scale analysis of social media data, robust estimation of population mental health is feasible at much higher resolutions, up to weekly estimates for counties. In the present work, we validate a pipeline that uses a sample of 1.2 billion Tweets from 2 million geo-located users to estimate mental health changes for the two leading mental health conditions, depression and anxiety. We find moderate to large associations between the language-based mental health assessments and survey scores from Gallup for multiple levels of granularity, down to the county-week (fixed effects β = .25 to 1.58; p<.001). Language-based assessment allows for the cost-effective and scalable monitoring of population mental health at weekly time scales. Such spatially fine-grained time series are well suited to monitor effects of societal events and policies as well as enable quasi-experimental study designs in population health and other disciplines. Beyond mental health in the U.S., this method generalizes to a broad set of psychological outcomes and allows for community measurement in under-resourced settings where no traditional survey measures - but social media data - are available.