Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

Johannes C. Eichstaedt*, Hansen Andrew Schwartz, Margaret L. Kern, Gregory Park, Darwin R. Labarthe, Raina M. Merchant, Sneha Jha, Megha Agrawal, Lukasz A. Dziurzynski, Maarten Sap, Christopher Weeg, Emily E. Larson, Lyle H. Ungar, Martin E.P. Seligman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

229 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)159-169
Number of pages11
JournalPsychological Science
Volume26
Issue number2
DOIs
StatePublished - Feb 14 2015

Keywords

  • big data
  • emotions
  • heart disease
  • language
  • open data
  • open materials
  • risk factors
  • social media
  • well-being

ASJC Scopus subject areas

  • Psychology(all)

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