The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Lloyd-Jones, D. M., Hong, Y., Labarthe, D., Mozaffarian, D., Appel, L. J., Van Horn, L., Greenlund, K., Daniels, S., Nichol, G., Tomaselli, G. F., et al. (2010) · Circulation · doi:10.1161/circulationaha.109.192703 · Cited by 4,750
Self-citationText as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · cited 2×
“Because words can have multiple senses, act as multiple parts of speech, and be used in the context of irony or negation, it is important to gauge empirically how well such lists of words measure what is intended (Grimmer & Stewart, 2013).”
BackgroundPsychological Aspects of Natural Language Use: Our Words, Our Selves
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624
Does positive affect influence health?
Pressman, S. D., & Cohen, S. (2005) · Psychological Bulletin · doi:10.1037/0033-2909.131.6.925 · Cited by 2,087
Meta-Analysis: Recent Developments in Quantitative Methods for Literature Reviews
Rosenthal, R., & DiMatteo, M. R. (2001) · Annual Review of Psychology · doi:10.1146/annurev.psych.52.1.59 · Cited by 1,780
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Self-citationThe Development and Psychometric Properties of LIWC2015
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015) · University of Texas at Austin · doi:10.15781/t25p41 · Cited by 1,674
Chida, Y., & Steptoe, A. (2008) · Psychosomatic Medicine · doi:10.1097/psy.0b013e31818105ba · Cited by 1,592 · cited 5×
“Although less is known at the individual level about the protective effects of positive psychological variables than about the risk associated with negative variables, our findings align with a growing body of research supporting the cardiovascular health benefits of psychological wellbeing (Boehm & Kubzansky, in pres…”
BackgroundInfluentialEysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351
The Undoing Effect of Positive Emotions
Fredrickson, B. L., Mancuso, R. A., Branigan, C., & Tugade, M. M. (2000) · Motivation and Emotion · doi:10.1023/a:1010796329158 · Cited by 1,290
Boehm, J. K., & Kubzansky, L. D. (2012) · Psychological Bulletin · doi:10.1037/a0027448 · Cited by 909
Personality, Well-Being, and Health*
Friedman, H. S., & Kern, M. L. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115123 · Cited by 530
Self-citationSocial media as a measurement tool of depression in populations
De Choudhury, M., Counts, S., & Horvitz, E. (2013) · Proceedings of the 5th Annual ACM Web Science Conference · doi:10.1145/2464464.2464480 · Cited by 497
Representation and communication: challenges in interpreting large social media datasets
Rost, M., Barkhuus, L., Cramer, H., & Brown, B. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441817 · Cited by 78
Cited by
Advances in subjective well-being research
Diener, E., Oishi, S., & Tay, L. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0307-6 · Cited by 2,003
Positive Affect and Health: What Do We Know and Where Next Should We Go?
Pressman, S. D., Jenkins, B. N., & Moskowitz, J. T. (2019) · Annual Review of Psychology · doi:10.1146/annurev-psych-010418-102955 · Cited by 452
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
The Ethics of Digital Well-Being: A Thematic Review
Burr, C., Taddeo, M., & Floridi, L. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00175-8 · Cited by 374
Using large language models in psychology
Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., et al. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00241-5 · Cited by 369
Self-citation
Metadata
- Authors
- 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.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2015-01-20
- Volume
- 26
- Issue
- 2
- Pages
- 159-169
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2118778378
- Cited by
- 486
- References
- 15
- FWCI
- 110.55
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
