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Psychological Language on Twitter Predicts County-Level Heart Disease Mortality

Overview of attention for article published in Psychological Science, January 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#28 of 4,317)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Citations

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378 Dimensions

Readers on

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704 Mendeley
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

X Demographics

The data shown below were collected from the profiles of 1,112 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 704 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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 665 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 140 20%
Student > Master 109 15%
Researcher 89 13%
Student > Bachelor 62 9%
Student > Doctoral Student 47 7%
Other 152 22%
Unknown 105 15%
Readers by discipline Count As %
Psychology 220 31%
Computer Science 87 12%
Social Sciences 57 8%
Medicine and Dentistry 47 7%
Business, Management and Accounting 33 5%
Other 105 15%
Unknown 155 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1095. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 February 2024.
All research outputs
#14,006
of 25,579,912 outputs
Outputs from Psychological Science
#28
of 4,317 outputs
Outputs of similar age
#101
of 361,087 outputs
Outputs of similar age from Psychological Science
#2
of 52 outputs
Altmetric has tracked 25,579,912 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,317 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 85.9. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,087 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.