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

heart disease risk factors well-being language big data emotions social media open data open materials

Get full access to this article

View all access options for this article.

References

Auchincloss

A. H.

Gebreab

S. Y.

Mair

Diez Roux

A. V.

(2012). A review of spatial methods in epidemiology, 2000–2010. Annual Review of Public Health, 33, 107–122. doi:10.1146/annurev-publhealth-031811-124655

Boehm

J. K.

Kubzansky

L. D.

(2012). The heart’s content: The association between positive psychological well-being and cardiovascular health. Psychological Bulletin, 138, 655–691. doi:10.1037/a0027448

Boehm

J. K.

Kubzansky

L. D.

(in press). Positive psychological well-being and cardiovascular disease. In Kop

Katzel

Waldstein

(Eds.), Cardiovascular behavioral medicine. New York, NY: Springer.

Centers for Disease Control and Prevention. (2009). Behavioral Risk Factor Surveillance System annual survey data. Retrieved from http://www.cdc.gov/brfss/annual_data/annual_2009.htm

Centers for Disease Control and Prevention. (2010a). Behavioral Risk Factor Surveillance System annual survey data. Retrieved from http://www.cdc.gov/brfss/annual_data/annual_2010.htm

Centers for Disease Control and Prevention. (2010b). Underlying cause of death 1999-2010. CDC WONDER online database [Data set]. Retrieved from http://wonder.cdc.gov/ucd-icd10.html

Chaix

Merlo

Evans

Leal

Havard

(2009). Neighbourhoods in eco-epidemiologic research: Delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Social Science & Medicine, 69, 1306–1310. doi:10.1016/j.socscimed.2009.07.018

Chew

Eysenbach

(2010). Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE, 5(11), Article e14118. Retrieved from http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014118

Chida

Steptoe

(2008). Positive psychological well-being and mortality: A quantitative review of prospective observational studies. Psychosomatic Medicine, 70, 741–756. doi:10.1097/PSY.0b013e31818105ba

10.

Chida

Steptoe

(2009). The association of anger and hostility with future coronary heart disease: A meta-analytic view of prospective evidence. Journal of the American College of Cardiology, 53, 936–946. doi:10.1016/j.jacc.2008.11.044

11.

Cohen

D. A.

Farley

T. A.

Mason

(2003). Why is poverty unhealthy? Social and physical mediators. Social Science & Medicine, 57, 1631–1641. doi:10.1016/S0277-9536(03)00015-7

12.

De Choudhury

Counts

Horvitz

(2013). Social media as a measurement tool of depression in populations. In WebSci ’13: Proceedings of the 5th Annual ACM Web Science Conference (pp. 47–56). doi:10.1145/2464464.2464480

13.

de Quincey

Kostkova

(2009). Early warning and outbreak detection using social networking websites: The potential of Twitter. In Kostkova

(Ed.), Electronic Healthcare: Second International ICST Conference, eHealth 2009, Istanbul, Turkey, September 23–25, 2009 (pp. 21–24). doi:10.1007/978-3-642-11745-9_4

14.

Diez Roux

A. V.

Mair

(2010). Neighborhoods and health. Annals of the New York Academy of Sciences, 1186, 125–145. doi:10.1111/j.1749-6632.2009.05333.x

15.

Eysenbach

(2009). Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of Medical Internet Research, 11(1), Article e11. Retrieved from http://www.jmir.org/2009/1/e11/.

16.

Ford

E. S.

Capewell

(2011). Proportion of the decline in cardiovascular mortality disease due to prevention versus treatments: Public health versus clinical care. Annual Review of Public Health, 32, 5–22. doi:10.1146/annurev-publhealth-031210-101211

17.

Fox

Zickurh

Smith

(2009). Twitter and status updating, fall 2009. Retrieved from Pew Research Internet Project Web site: http://www.pewinternet.org/2009/10/21/twitter-and-status-updating-fall-2009

18.

Fredrickson

B. L.

Mancuso

R. A.

Branigan

Tugade

M. M.

(2000). The undoing effects of positive emotions. Motivation and Emotion, 24, 237–258. doi:10.1023/A:1010796329158

19.

Friedman

H. S.

Kern

M. L.

(2014). Personality, well-being, and health. Annual Review of Psychology, 65, 719–742. doi:10.1146/annurev-psych-010213-115123

20.

Ginsberg

Mohebbi

M. H.

Patel

R. S.

Brammer

Smolinski

M. S.

Brilliant

(2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012–1014. doi:10.1038/nature07634

21.

Grimmer

Stewart

B. M.

(2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21, 267–297. doi:10.1093/pan/mps028

22.

Howell

Kern

M. L.

Lyubomirsky

(2007). Health benefits: Meta-analytically determining the impact of well-being on objective health outcomes. Health Psychology Review, 1, 83–136. doi:10.1080/17437190701492486

23.

Labarthe

D. R.

(2010). Epidemiology and prevention of cardiovascular disease: A global challenge. Sudbury, MA: Jones & Bartlett.

24.

Lett

H. S.

Blumenthal

J. A.

Babyak

M. A.

Sherwood

Strauman

Robins

Newman

M. F.

(2004). Depression as a risk factor for coronary artery disease: Evidence, mechanisms, and treatment. Psychosomatic Medicine, 66, 305–315. doi:10.1097/01.psy.0000126207.43307.c0

25.

Leyland

A. H.

(2005). Socioeconomic gradients in the prevalence of cardiovascular disease in Scotland: The roles of composition and context. Journal of Epidemiology & Community Health, 59, 799–803. doi:10.1136/jech.2005.034017

26.

Lloyd-Jones

D. M.

Hong

Labarthe

Mozaffarian

Appel

L. J.

Van Horn

. . . Rosamond

W. D.

(2010). Defining and setting national goals for cardiovascular health promotion and disease reduction: The American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation, 121, 585–613. doi:10.1161/CIRCULATIONAHA.109.192703

27.

Lochner

K. A.

Kawachi

Brennan

R. T.

Buka

S. L.

(2003). Social capital and neighborhood mortality rates in Chicago. Social Science & Medicine, 56, 1797–1805. doi:10.1016/S0277-9536(02)00177-6

28.

Menezes

A. R.

Lavie

C. J.

Milani

R. V.

O’Keefe

Lavie

T. J.

(2011). Psychological risk factors and cardiovascular disease: Is it all in your head? Postgraduate Medicine, 123, 165–176. doi:10.3810/pgm.2011.09.2472

29.

Mislove

Lehmann

Ahn

Y.-Y.

Onnela

J. P.

Rosenquist

J. N.

(2011). Understanding the demographics of Twitter users. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (pp. 554–557). Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2816/3234

30.

Paul

M. J.

Dredze

(2011a). A model for mining public health topics from Twitter. Retrieved from the Johns Hopkins University Web site: http://www.cs.jhu.edu/~mpaul/files/2011.tech.twitter_health.pdf

31.

Paul

M. J.

Dredze

(2011b). You are what you tweet: Analyzing Twitter for public health. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (pp. 265–272). Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2880/3264

32.

Pennebaker

J. W.

Chung

C. K.

Ireland

Gonzales

Booth

R. J.

(2007). The development and psychometric properties of LIWC2007. Austin, TX: LIWC.net.

33.

Pennebaker

J. W.

Mehl

M. R.

Niederhoffer

K. G.

(2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577. doi:10.1146/annurev.psych.54.101601.145041

34.

Pierce

J. R.

Denison

A. V.

(2010). Accuracy of death certificates and the implications for studying disease burdens. In Preedy

V. R.

Watson

R. R.

(Eds.), Handbook of disease burdens and quality of life measures (pp. 329–344). New York, NY: Springer.

35.

Pressman

S. D.

Cohen

(2005). Does positive affect influence health? Psychological Bulletin, 131, 925–971. doi:10.1037/0033-2909.131.6.925

36.

Riva

Gauvin

Barnett

T. A.

(2007). Toward the next generation of research into small area effects on health: A synthesis of multilevel investigations published since July 1998. Journal of Epidemiology & Community Health, 61, 853–861. doi:10.1136/jech.2006.050740

37.

Roest

A. M.

Martens

E. J.

de Jonge

Denollet

(2010). Anxiety and risk of incident coronary heart disease: A meta-analysis. Journal of the American College of Cardiology, 56, 38–46. doi:10.1016/j.jacc.2010.03.034

38.

Rosenthal

DiMatteo

M. R.

(2001). Meta-analysis: Recent developments in quantitative methods for literature reviews. Annual Review of Psychology, 52, 59–82. doi:10.1146/annurev.psych.52.1.59

39.

Rost

Barkhuus

Cramer

Brown

(2013, February). Representation and communication: Challenges in interpreting large social media datasets. Paper presented at the 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013), San Antonio, TX.

40.

Rowe

J. W.

Kahn

R. L.

(1987). Human aging: Usual and successful. Science, 237, 143–149. doi:10.1126/science.3299702

41.

Rugulies

(2002). Depression as a predictor for coronary heart disease: A review and meta-analysis. American Journal of Preventive Medicine, 23, 51–61. doi:10.1016/S0749-3797(02)00439-7

42.

Salathé

Freifeld

C. C.

Mekaru

S. R.

Tomasulo

A. F.

Brownstein

J. S.

(2013). Influenza A (H7N9) and the importance of digital epidemiology. New England Journal of Medicine, 369, 401–404. doi:10.1056/NEJMp1307752

43.

Schwartz

H. A.

Eichstaedt

J. C.

Kern

M. L.

Dziurzynski

Lucas

R. E.

Agrawal

. . . Ungar

L. H.

(2013). Characterizing geographic variation in well-being using tweets. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (pp. 583–591). Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6138/6398.

44.

Schwartz

H. A.

Eichstaedt

J. C.

Kern

M. L.

Dziurzynski

Ramones

S. M.

Agrawal

. . . Ungar

L. H.

(2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE, 8(9), Article e73791. Retrieved from http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073791

45.

Seifter

Schwarzwalder

Geis

Aucott

(2010). The utility of “Google Trends” for epidemiological research: Lyme disease as an example. Geospatial Health, 4, 135–137.

46.

St Louis

Zorlu

(2012). Can Twitter predict disease outbreaks? British Medical Journal, 344, Article e2353. Retrieved from http://www.bmj.com/content/344/bmj.e2353

47.

Stone

P. J.

Dunphy

D. C.

Smith

M. S.

Ogilvie

D. M.

(1966). The general inquirer: A computer approach to content analysis. Cambridge, MA: MIT Press.

48.

Tay

Tan

Diener

Gonzalez

(2013). Social relations, health behaviors, and health outcomes: A survey and synthesis. Applied Psychology: Health and Well-Being, 5, 28–78.

49.

U.S. Census Bureau. (2009). Selected social characteristics in the United States: 2009. American Community Survey 1-year estimates. Retrieved from http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_09_1YR_DP2&prodType=table

50.

U.S. Census Bureau. (2010). Profile of general population and housing characteristics: 2010 demographic profile data. Retrieved from http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_DP_DPDP1&prodType=table

51.

World Health Organization. (1992). Atherosclerotic heart disease. In International statistical classification of diseases and related health problems (10th ed.). Retrieved from http://apps.who.int/classifications/icd10/browse/2010/en#/I25.1

52.

World Health Organization. (2011). Global status report on noncommunicable diseases 2010. Description of the global burden of NCDs, their risk factors and determinants. Retrieved from http://www.who.int/nmh/publications/ncd_report2010/en/

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.58 MB

0.02 MB

0.65 MB