Sage Journals: Discover world-class research

Abstract

The novel coronavirus (Covid-19) that plagued the world in 2020 also brought with it the need to rapidly disseminate information to the public to encourage health-related behavior change. This study examines Covid-19-related Twitter messaging disseminated by the Centers for Disease Control and Prevention and the World Health Organization from February 29 to September 22, 2020. The research examined tweets from four constructed weeks, with the first 2 weeks representing days prior to President Donald Trump’s announcement of U.S. withdrawal from WHO, and the second 2-week period after the announcement. The Health Belief Model was used as the theoretical foundation for this study. Frequencies and chi-square analyses revealed less of an overall focus on barriers but no significant differences in messages for tweets related to consequences, benefits, and barriers. Significant differences (p < .01) were found in engagement messaging for the second 2-week period.

Keywords

Covid-19 Health Belief Model tweets constructed week CDC WHO

Get full access to this article

View all access options for this article.

References

Biswas

(2013). Health organizations’ use of social media tools during a pandemic situation: An H1N1 flu context. Journal of New Communications Research, 5(1), 46–81.

Brenner

Smith

(2013). 72% of online adults are social networking site users. Pew Internet & American Life Project.

Brett

E. I.

Stevens

E. M.

Wagener

T. L.

Leavens

E. L.

Morgan

T. L.

Cotton

W. D.

Hébert

E. T.

(2019). A content analysis of JUUL discussions on social media: Using Reddit to understand patterns and perceptions of JUUL use. Drug and Alcohol Dependence, 194, 358–362.

Caron

(2019). Pinterest restricts vaccine search results to curb spread of misinformation. New York Times. https://www.nytimes.com/2019/02/23/health/pinterest-vaccination-searches.html

Freelon

(2013). ReCal OIR: Ordinal, interval, and ratio intercoder reliability as a web service. International Journal of Internet Science, 8(1), 10–16.

Gui

Wang

Kou

Reynolds

T. L.

Chen

Mei

Zheng

(2017). Understanding the patterns of health information dissemination on social media during the Zika outbreak [Symposium]. AMIA Annual Symposium Proceedings, Washington DC (Vol. 2017, p. 820). American Medical Informatics Association.

Guidry

J. P.

Coman

I. A.

Vraga

E. K.

O’Donnell

N. H.

Sreepada

(2020a). (S)pin the flu vaccine: Recipes for concern. Vaccine, 38(34), 5498–5506.

Guidry

J. P.

Meganck

S. L.

Lovari

Messner

Medina-Messner

Sherman

Adams

(2020b). Tweeting about #diseases and #publichealth: Communicating global health issues across nations. Health Communication, 35(9), 1137–1145.

Gurman

T. A.

Clark

(2016). #ec: Findings and implications from a quantitative content analysis of tweets about emergency contraception. Digital health, 2, 2055207615625035.

10.

Hochbaum

G.M.

(1958). Public participation in medical screening programs: A socio-psychological study. US Department of Health, Education and Welfare.

11.

Houts

P. S.

Doak

C. C.

Doak

L. G.

Loscalzo

M. J.

(2006). The role of pictures in improving health communication: A review of research on attention, comprehension, recall, and adherence. Patient Education and Counseling, 61(2), 173–190.

12.

Kim

Liu

B. F.

(2012). Are all crises opportunities? A comparison of how corporate and government organizations responded to the 2009 flu pandemic. Journal of Public Relations Research, 24(1), 69–85.

13.

Medford

R. J.

Saleh

S. N.

Sumarsono

Perl

T. M.

Lehmann

C. U.

(2020). An “infodemic”: Leveraging high-volume Twitter data to understand public sentiment for the COVID-19 Outbreak. medRxiv.

14.

Miller

C. A.

Guidry

J. P.

Fuemmeler

B. F.

(2019). Breast cancer voices on pinterest: Raising awareness or just an inspirational image? Health Education & Behavior, 46(2_suppl), 49S–58S.

15.

Rosenstock

I. M.

(1960). What research in motivation suggests for public health. American Journal of Public Health and the Nation’s Health, 50, 295–302.

16.

Sehl

(2020). 28 pinterest statistics marketers should know in 2020. https://blog.hootsuite.com/pinterest-statistics-for-business/

17.

Skinner

C. S.

Tiro

J. U.

Champion

V. L.

(2015). The health belief model. In Glanz

Rimer

B.K.

Viswanath

(Eds.), Health behavior: Theory, research and practice (5th ed., pp. 75–94). Jossey-Bass.

18.

Suh

Hong

Pirolli

Chi

E. H.

(2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network [Conference session]. 2010 IEEE Second International Conference on Social Computing, Minneapolis (pp. 177–184). IEEE.

19.

Tang

Park

S. E.

(2017). Sun exposure, tanning beds, and herbs that cure: an examination of skin cancer on Pinterest. Health Communication, 32(10), 1192–1200.

20.

Yum

(2020). Social network analysis for Coronavirus (COVID-19) in the United States. Social Science Quarterly, 101(4), 1642–1647.

A Comparison of Covid-19-Related Tweets Disseminated by the Centers for Disease Control and Prevention and the World Health Organization During the Initial Months of the Pandemic

Abstract

Keywords

Get full access to this article

References