Title |
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
|
---|---|
Published in |
Psychological Science, January 2015
|
DOI | 10.1177/0956797614557867 |
Pubmed ID | |
Authors |
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 |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 133 | 12% |
Thailand | 123 | 11% |
United Kingdom | 32 | 3% |
Japan | 17 | 2% |
Canada | 17 | 2% |
Argentina | 16 | 1% |
Spain | 15 | 1% |
Germany | 15 | 1% |
France | 9 | <1% |
Other | 125 | 11% |
Unknown | 609 | 55% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 950 | 86% |
Scientists | 97 | 9% |
Practitioners (doctors, other healthcare professionals) | 48 | 4% |
Science communicators (journalists, bloggers, editors) | 16 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 17 | 2% |
United Kingdom | 6 | <1% |
Canada | 3 | <1% |
Spain | 2 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Switzerland | 1 | <1% |
Germany | 1 | <1% |
Finland | 1 | <1% |
Other | 6 | <1% |
Unknown | 664 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 140 | 20% |
Student > Master | 109 | 16% |
Researcher | 88 | 13% |
Student > Bachelor | 62 | 9% |
Student > Doctoral Student | 47 | 7% |
Other | 152 | 22% |
Unknown | 105 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 218 | 31% |
Computer Science | 87 | 12% |
Social Sciences | 57 | 8% |
Medicine and Dentistry | 47 | 7% |
Business, Management and Accounting | 34 | 5% |
Other | 105 | 15% |
Unknown | 155 | 22% |