Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

TLDR

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. The study aimed to use Twitter language to characterize community‑level psychological correlates of age‑adjusted atherosclerotic heart disease mortality. We analyzed language expressed on Twitter to identify patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—as risk factors, and positive emotions and psychological engagement as protective factors. Language patterns indicating negative social relationships, disengagement, and negative emotions—particularly anger—were significant risk factors for AHD mortality, while positive emotions and psychological engagement were protective; these associations remained significant after controlling for income and education, and a regression model based solely on Twitter language predicted AHD mortality more accurately than a model incorporating ten common demographic, socioeconomic, and health risk factors, demonstrating that community psychological characteristics captured via social media are strong markers of cardiovascular mortality.

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.