Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

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...

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Published inPsychological science Vol. 26; no. 2; pp. 159 - 169
Main Authors Eichstaedt, Johannes C., Schwartz, Hansen Andrew, Kern, Margaret L., Park, Gregory, Labarthe, Darwin R., Merchant, Raina M., Jha, Sneha, Agrawal, Megha, Dziurzynski, Lukasz A., Sap, Maarten, Weeg, Christopher, Larson, Emily E., Ungar, Lyle H., Seligman, Martin E. P.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.02.2015
SAGE PUBLICATIONS, INC
Subjects
Atherosclerosis
Cardiovascular disease
Coronary Artery Disease - mortality
Coronary Artery Disease - psychology
Cross-Sectional Studies
Data Collection - statistics & numerical data
Emotions
Female
Heart disease
Humans
Language
Male
Models, Psychological
Models, Statistical
Public health
Regression Analysis
Risk Factors
Smoking
Social media
Social Media - statistics & numerical data
Social networks
Social psychology
Twitter
United States - epidemiology
Well-being
well-being
risk factors
emotions
heart disease
open materials
language
big data
open data
social media
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Summary: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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Author Contributions: J. C. Eichstaedt led the project. J. C. Eichstaedt and H. A. Schwartz conceived of the study. H. A. Schwartz, J. C. Eichstaedt, G. Park, S. Jha, M. Agrawal, L. A. Dziurzynski, and M. Sap handled data acquisition and processing, development of the prediction models, and data analyses. J. C. Eichstaedt, M. L. Kern, H. A. Schwartz, and G. Park drafted the manuscript. D. R. Labarthe, R. M. Merchant, L. H. Ungar, and M. E. P. Seligman provided critical revisions. C. Weeg and E. E. Larson helped acquire, process, and analyze county-level information. All authors approved the final version of the manuscript for submission. L. H. Ungar and M. E. P. Seligman contributed equally to this article.
ISSN:0956-7976
1467-9280
DOI:10.1177/0956797614557867