Psychological Language on Twitter Predicts County-Level Heart Disease

Date Published: 
January 2015
Eichstaedt, JC, Schwartz, HA, Kern, ML, Park, G, Labarthe, D, Merchant, RM, Jha, S, Agrawal, M, Dziurzynski, LA, Sap, M, Weeg, C, Larson, EE, Ungar, LH, & Seligman, MEP

Hostility and stress are known risk factors for heart disease, but are costly to assess on a large scale. Therefore, researchers used language expressed on Twitter to characterize county-level psychological correlates of age-adjusted mortality from artherosclerotic heart disease. Language patterns reflecting negative social relationships, disengagement, and negative emotions emerged as risk factors; and positive emotions and engagement emerged as protective factors against heart disease. Thse correlations remained significant after controlling for income and education levels. A cross-sectional, regression model based only on Twitter language predicted heart disease better than a model that combined ten well-known risk factors such as smoking, diabetes, obesity, and hypertension.

Health Assets: 
Atherosclerosis and Calcification, Cardiovascular Health, Emotional Outlook on Life, Positive Emotions, Social Relations
Health Conditions: 
Coronary Heart Disease, Coronary Heart Disease (Mortality), Life Outcomes
RWJF Grant-Funded: