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Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

Johannes C. Eichstaedt¹; Hansen Andrew Schwartz^1,2; Margaret L. Kern^1,3; Gregory Park¹; Darwin R. Labarthe⁴; Raina M. Merchant⁵; Sneha Jha²; Megha Agrawal²; Lukasz A. Dziurzynski¹; Maarten Sap¹; Christopher Weeg¹; Emily E. Larson¹; Lyle H. Ungar^1,2; Martin E.P. Seligman¹

¹ Department of Psychology, University of Pennsylvania · ² Department of Computer and Information Science, University of Pennsylvania · ³ Graduate School of Education, University of Melbourne · ⁴ School of Medicine, Northwestern University · ⁵ Department of Emergency Medicine, University of Pennsylvania

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.

DOI

10.1177/0956797614557867

Volume

26 (2)

Pages

159-169

Language

en

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Sources

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