Cited by 166Communication and COVID-19 ImpactPublic Relations and Crisis CommunicationSocial Media in Health Education

Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison

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Sesagiri Raamkumar, Aravind, Tan, Soon Guan, Wee, Hwee Lin·2020·Journal of Medical Internet Research

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Abstract

Background: The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly communicate and disseminate pandemic response measures to the public. Understanding of communication strategies across different PHAs and examining the public response on the social media landscapes can help improve practices for disseminating information to the public. Objective: This study aims to examine COVID-19-related outreach efforts of PHAs in Singapore, the United States, and England, and the corresponding public response to these outreach efforts on Facebook. Methods: Posts and comments from the Facebook pages of the Ministry of Health (MOH) in Singapore, the Centers for Disease Control and Prevention (CDC) in the United States, and Public Health England (PHE) in England were extracted from January 1, 2019, to March 18, 2020. Posts published before January 1, 2020, were categorized as pre-COVID-19, while the remaining posts were categorized as peri-COVID-19 posts. COVID-19-related posts were identified and classified into themes. Metrics used for measuring outreach and engagement were frequency, mean posts per day (PPD), mean reactions per post, mean shares per post, and mean comments per post. Responses to the COVID-19 posts were measured using frequency, mean sentiment polarity, positive to negative sentiments ratio (PNSR), and positive to negative emotions ratio (PNER). Toxicity in comments were identified and analyzed using frequency, mean likes per toxic comment, and mean replies per toxic comment. Trend analysis was performed to examine how the metrics varied with key events such as when COVID-19 was declared a pandemic. Results: The MOH published more COVID-19 posts (n=271; mean PPD 5.0) compared to the CDC (n=94; mean PPD 2.2) and PHE (n=45; mean PPD 1.4). The mean number of comments per COVID-19 post was highest for the CDC (mean CPP 255.3) compared to the MOH (mean CPP 15.6) and PHE (mean CPP 12.5). Six major themes were identified, with posts about prevention and safety measures and situation updates being prevalent across the three PHAs. The themes of the MOH’s posts were diverse, while the CDC and PHE posts focused on a few themes. Overall, response sentiments for the MOH posts (PNSR 0.94) were more favorable compared to response sentiments for the CDC (PNSR 0.57) and PHE (PNSR 0.55) posts. Toxic comments were rare (0.01%) across all PHAs. Conclusions: PHAs’ extent of Facebook use for outreach purposes during the COVID-19 pandemic varied among the three PHAs, highlighting the strategies and approaches that other PHAs can potentially adopt. Our study showed that social media analysis was capable of providing insights about the communication strategies of PHAs during disease outbreaks.

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  • There are few reports about the impact of these interventions and difficulties in defining misinformation.

    “There are few reports about the impact of these interventions 106,107 and difficulties in defining misinformation 108 .”

    From Digital technologies in the public-health response to COVID-19 (2020) · cites this paper as [107]

    The Introduction (Social Media Use During Pandemics; page unavailable) says there is a lack of studies comparing public-health authorities’ social-media outreach and the public’s responses. That supports a narrower research-gap claim, but it does not establish that few reports assess the impact of the particular interventions mentioned in the citing sentence. The separate claim about difficulties defining misinformation is attached to citation 108, not this source. (LLM, conf=0.77)

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References 32 · Cited by 166

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  1. [3]Holshue ML DeBolt C Lindquist S Lofy KH Wiesman J Bruce H Spitters C Ericson K Wilkerson S Tural A Diaz G Cohn A Fox L Patel A Gerber SI Kim L Tong S Lu X Lindstrom S Pallansch MA Weldon WC Biggs HM Uyeki TM Pillai SK First case of 2019 novel coronavirus in the United States N Engl J Med 2020 3 05 382 10 929 936 10.1056/nejmoa2001191 32004427 PMC7092802 · DOI · Paper page
  2. [4]Moss P Barlow G Easom N Lillie P Samson A Lessons for managing high-consequence infections from first COVID-19 cases in the UK Lancet 2020 3 14 395 10227 e46 10.1016/S0140-6736(20)30463-3 32113507 32113507 PMC7133597 · DOI
  3. [6]Lee V Chiew C Khong W Interrupting transmission of COVID-19: lessons from containment efforts in Singapore J Travel Med 2020 3 13 e 10.1093/jtm/taaa039 32167146 PMC7107552 32167146 · DOI
  4. [16]Hoffman SJ Silverberg SL Delays in global disease outbreak responses: lessons from H1N1, Ebola, and Zika Am J Public Health 2018 3 108 3 329 333 10.2105/AJPH.2017.304245 29345996 29345996 PMC5803810 · DOI
  5. [17]Holmes BJ Communicating about emerging infectious disease: the importance of research Health Risk Soc 2008 8 10 4 349 360 10.1080/13698570802166431 · DOI
  6. [18]Vaughan E Tinker T Effective health risk communication about pandemic influenza for vulnerable populations Am J Public Health 2009 10 99 S2 S324 S332 10.2105/ajph.2009.162537 19797744 PMC4504362 · DOI
  7. [19]Wong JEL Leo YS Tan CC COVID-19 in Singapore-current experience: critical global issues that require attention and action JAMA 2020 2 20 e 10.1001/jama.2020.2467 32077901 32077901 · DOI
  8. [20]Vijaykumar S Jin Y Nowak G Social media and the virality of risk: the risk amplification through media spread (RAMS) model J Homeland Secur Emerg Manage 2015 653 10.1515/jhsem-2014-0072 · DOI
  9. [21]Vijaykumar S Meurzec R Jayasundar K Pagliari C Fernandopulle Y What’s buzzing on your feed? Health authorities’ use of Facebook to combat Zika in Singapore J Am Med Informatics Assoc 2017 24 6 1159 10.1093/jamia/ocx028 PMC7651910 28449047 · DOI
  10. [22]Lwin M Lu J Sheldenkar A Schulz P Strategic uses of Facebook in Zika outbreak communication: implications for the crisis and emergency risk communication model Int J Environ Res Public Health 2018 9 10 15 9 1974 10.3390/ijerph15091974 30201929 PMC6163817 30201929 · DOI
  11. [23]Mamidi R Miller M Banerjee T Romine W Sheth A Identifying key topics bearing negative sentiment on Twitter: insights concerning the 2015-2016 Zika epidemic JMIR Public Health Surveill 2019 6 04 5 2 e11036 10.2196/11036 31165711 31165711 PMC6682293 · DOI
  12. [24]de Araujo DHM de Carvalho EA da Motta CLR da Silva Borges MR Gomes JO de Carvalho PVR Social networks applied to Zika and H1N1 epidemics: a systematic review Springer, Cham 2019 679 10.1007/978-3-319-96089-0_74 · DOI
  13. [25]Strekalova YA Health risk information engagement and amplification on social media Health Educ Behav 2017 4 44 2 332 339 10.1177/1090198116660310 27413028 27413028 · DOI
  14. [26]Vijaykumar S Nowak G Himelboim I Jin Y Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter Am J Infect Control 2018 5 46 5 549 557 10.1016/j.ajic.2017.10.015 29306490 29306490 · DOI
  15. [28]Glik DC Risk communication for public health emergencies Annu Rev Public Health 2007 28 33 54 10.1146/annurev.publhealth.28.021406.144123 17222081 17222081 · DOI
  16. [34]Erlingsson C Brysiewicz P A hands-on guide to doing content analysis Afr J Emerg Med 2017 9 7 3 93 99 10.1016/J.AFJEM.2017.08.001 30456117 PMC6234169 · DOI
  17. [35]Hutto C Gilbert E VADER: a parsimonious rule-based model for sentiment analysis of social media text AAI Publications, Eighth International AAAI Conference on Weblogs and Social Media 2014 A
  18. [36]Plutchik R Emotions: a general psychoevolutionary theory Approaches to Emotion 2014 5 22 London Psychology Press A
  19. [37]Felbo B Mislove A Søgaard A Rahwan I Lehmann S Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm 2017 2017 Conference on Empirical Methods in Natural Language Processing September 2017 Copenhagen, Denmark 10.18653/v1/d17-1169 · DOI
  20. [39]Al-Hajjar D Syed A Applying sentiment and emotion analysis on brand tweets for digital marketing 2015 12 21 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) 3-5 Nov 2015 Amman, Jordan 10.1109/aeect.2015.7360592 · DOI
  21. [40]Daniulaityte R Lamy F Smith G Nahhas R Carlson R Thirunarayan K Martins S Boyer E Sheth A "Retweet to pass the blunt": analyzing geographic and content features of cannabis-related tweeting across the United States J Stud Alcohol Drugs 2017 11 78 6 910 915 10.15288/jsad.2017.78.910 29087826 29087826 PMC5668996 · DOI
  22. [41]Cheng J Bernstein M Danescu-Niculescu-Mizil C Leskovec J Anyone can become a troll: causes of trolling behavior in online discussions 2017 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing 2017 New York, NY 1217 1230 10.1145/2998181.2998213 PMC5791909 29399664 · DOI

Authors
Sesagiri Raamkumar, A., Tan, S. G., & Wee, H. L.
Affiliations
National University of Singapore (SG)
Corresponding author
Wee, Hwee Lin
Venue
Journal of Medical Internet Research
Published
2020-05-19
Volume
22
Issue
5
Pages
e19334
ISSN
1438-8871 (ISSN-L)
OpenAlex
W3024743749
Cited by
166
References
32
Citations by year
2026: 2 · 2025: 7 · 2024: 16 · 2023: 29 · 2022: 29 · 2021: 57 · 2020: 26
FWCI
42.29
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National University of Singapore
  • National University Health System
Topics
  • Social Sciences › Communication › Communication and COVID-19 Impact
  • Social Sciences › Communication › Public Relations and Crisis Communication
  • Social Sciences › Health › Social Media in Health Education
Keywords
COVID-19 pandemic, public health authorities, Facebook, social media outreach, public response, public health communication, social media engagement, sentiment analysis, toxic comments, cross-national comparison, CDC, Public Health England, public trust in government, misinformation, vaccine hesitancy
MeSH terms
Betacoronavirus, COVID-19, SARS-CoV-2, England, Humans, Pneumonia, Viral, Public Health, Singapore, United States, Coronavirus Infections, Information Dissemination, Pandemics, Social Media

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