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

doi:10.1177/0956797614557867

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

Preventive Medicine

Research output: Contribution to journal › Article › peer-review

323 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 language	English (US)
Pages (from-to)	159-169
Number of pages	11
Journal	Psychological Science
Volume	26
Issue number	2
DOIs	https://doi.org/10.1177/0956797614557867
State	Published - 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)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1177/0956797614557867

Cite this

Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., Jha, S., Agrawal, M., Dziurzynski, L. A., Sap, M., Weeg, C., Larson, E. E., Ungar, L. H., & Seligman, M. E. P. (2015). Psychological Language on Twitter Predicts County-Level Heart Disease Mortality. Psychological Science, 26(2), 159-169. https://doi.org/10.1177/0956797614557867

@article{42ffe2a773ba46a18f9bff130fc70a39,

title = "Psychological Language on Twitter Predicts County-Level Heart Disease 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.",

keywords = "big data, emotions, heart disease, language, open data, open materials, risk factors, social media, well-being",

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

note = "Funding Information: This work was supported by the Robert Wood Johnson Foundation{\textquoteright}s Pioneer Portfolio, through Exploring Concepts of Positive Health Grant 63597 (to M. E. P. Seligman), and by a grant from the Templeton Religion Trust. Publisher Copyright: {\textcopyright} The Author(s) 2014.",

year = "2015",

month = feb,

day = "14",

doi = "10.1177/0956797614557867",

language = "English (US)",

volume = "26",

pages = "159--169",

journal = "Psychological Science",

issn = "0956-7976",

publisher = "SAGE Publications Inc.",

number = "2",

}

TY - JOUR

T1 - Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

AU - Eichstaedt, Johannes C.

AU - Schwartz, Hansen Andrew

AU - Kern, Margaret L.

AU - Park, Gregory

AU - Labarthe, Darwin R.

AU - Merchant, Raina M.

AU - Jha, Sneha

AU - Agrawal, Megha

AU - Dziurzynski, Lukasz A.

AU - Sap, Maarten

AU - Weeg, Christopher

AU - Larson, Emily E.

AU - Ungar, Lyle H.

AU - Seligman, Martin E.P.

N1 - Funding Information: This work was supported by the Robert Wood Johnson Foundation’s Pioneer Portfolio, through Exploring Concepts of Positive Health Grant 63597 (to M. E. P. Seligman), and by a grant from the Templeton Religion Trust. Publisher Copyright: © The Author(s) 2014.

PY - 2015/2/14

Y1 - 2015/2/14

N2 - 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.

AB - 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.

KW - big data

KW - emotions

KW - heart disease

KW - language

KW - open data

KW - open materials

KW - risk factors

KW - social media

KW - well-being

UR - http://www.scopus.com/inward/record.url?scp=84922758277&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922758277&partnerID=8YFLogxK

U2 - 10.1177/0956797614557867

DO - 10.1177/0956797614557867

M3 - Article

C2 - 25605707

AN - SCOPUS:84922758277

SN - 0956-7976

VL - 26

SP - 159

EP - 169

JO - Psychological Science

JF - Psychological Science

IS - 2

ER -

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this