Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

Type Journal Article Author Johannes C. Eichstaedt Author Hansen Andrew Schwartz Author Margaret L. Kern Author Gregory Park Author Darwin R. Labarthe Author Raina M. Merchant Author Sneha Jha Author Megha Agrawal Author Lukasz A. Dziurzynski Author Maarten Sap Author Christopher Weeg Author Emily E. Larson Author Lyle H. Ungar Author Martin E. P. Seligman URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433545/ Volume 26 Issue 2 Pages 159-169 Publication Psychological science ISSN 0956-7976 Date 2015-2 Extra PMID: 25605707 PMCID: PMC4433545 Journal Abbr Psychol Sci DOI 10.1177/0956797614557867 Accessed 2022-02-16 19:41:48 Library Catalog PubMed Central 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.