ArticlePsychological science2015
Psychological language on Twitter predicts county-level heart disease mortality.
Article in Psychological science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 1 of them a synthesis that pooled it.
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Who cites it
135 citing papers in PubMed, 1 synthesis or guideline pooled it, 507 citations in OpenAlex.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
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- Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023-2024): Observational Study.JMIR infodemiology · 2026Observational
- Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data.American heart journal plus : cardiology research and practice · 2026Article
- Psychological decoupling in responses to AI: emotional reactivity and behavioral discourse in digital environments.Frontiers in psychiatry · 2026Article
- Can an Algorithm Tell How Spiritual You Are? Using Generative Pretrained Transformers for Sophisticated Forms of Text Analysis.Journal of personality · 2025Article
- Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
- Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study.Journal of medical Internet research · 2024Article
- Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States.Preventive medicine · 2024Article
- Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.JMIR medical informatics · 2024Article
- Robust language-based mental health assessments in time and space through social media.NPJ digital medicine · 2024Article
- Omicron detection with large language models and YouTube audio data.medRxiv : the preprint server for health sciences · 2024Article
- Using Exogenous Social Media Exposure Measures to Assess the Effects of Smokeless Tobacco-Related Social Media Content on Smokeless Tobacco Sales in the United States.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Quantifying generalized trust in individuals and counties using language.Frontiers in social psychology · 2024Article
- Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models.PloS one · 2024Article
- Extracting factors associated with vaccination from Twitter data and mapping to behavioral models.Human vaccines & immunotherapeutics · 2023Article
- The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.Psychological methods · 2023Article
- Shocktainment Techniques in the Mirror, a British Daily Tabloid: Linguocultural Features of news and semantic-cognitive Analysis of Leading Concepts.Journal of psycholinguistic research · 2023Article
- Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study.JMIR infodemiology · 2023Article
75 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions-especially anger-emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.