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Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter.
Ramjee, Divya; Pollack, Catherine C; Charpignon, Marie-Laure; Gupta, Shagun; Rivera, Jessica Malaty; El Hayek, Ghinwa; Dunn, Adam G; Desai, Angel N; Majumder, Maimuna S.
  • Ramjee D; School of Public Affairs, American University, Washington, DC, United States.
  • Pollack CC; Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
  • Charpignon ML; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Gupta S; Comp Epi Dispersed Volunteer Research Network, Boston, MA, United States.
  • Rivera JM; Boston Children's Hospital, Boston, MA, United States.
  • El Hayek G; Harvard Medical School, Boston, MA, United States.
  • Dunn AG; Comp Epi Dispersed Volunteer Research Network, Boston, MA, United States.
  • Desai AN; School of Medical Sciences, The University of Sydney, Sydney, Australia.
  • Majumder MS; Department of Internal Medicine, Division of Infectious Diseases, University of California-Davis Health Medical Center, Sacramento, CA, United States.
J Med Internet Res ; 25: e40706, 2023 02 27.
Article in English | MEDLINE | ID: covidwho-2277667
ABSTRACT

BACKGROUND:

Throughout the COVID-19 pandemic, US Centers for Disease Control and Prevention policies on face mask use fluctuated. Understanding how public health communications evolve around key policy decisions may inform future decisions on preventative measures by aiding the design of communication strategies (eg, wording, timing, and channel) that ensure rapid dissemination and maximize both widespread adoption and sustained adherence.

OBJECTIVE:

We aimed to assess how sentiment on masks evolved surrounding 2 changes to mask guidelines (1) the recommendation for mask use on April 3, 2020, and (2) the relaxation of mask use on May 13, 2021.

METHODS:

We applied an interrupted time series method to US Twitter data surrounding each guideline change. Outcomes were changes in the (1) proportion of positive, negative, and neutral tweets and (2) number of words within a tweet tagged with a given emotion (eg, trust). Results were compared to COVID-19 Twitter data without mask keywords for the same period.

RESULTS:

There were fewer neutral mask-related tweets in 2020 (ß=-3.94 percentage points, 95% CI -4.68 to -3.21; P<.001) and 2021 (ß=-8.74, 95% CI -9.31 to -8.17; P<.001). Following the April 3 recommendation (ß=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (ß=3.43, 95% CI 1.61-5.26; P<.001), the percent of negative mask-related tweets increased. The quantity of trust-related terms decreased following the policy change on April 3 (ß=-.004, 95% CI -.004 to -.003; P<.001) and May 13 (ß=-.001, 95% CI -.002 to 0; P=.008).

CONCLUSIONS:

The US Twitter population responded negatively and with less trust following guideline shifts related to masking, regardless of whether the guidelines recommended or relaxed mask usage. Federal agencies should ensure that changes in public health recommendations are communicated concisely and rapidly.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Communication / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: 40706

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Communication / Social Media / COVID-19 Type of study: Experimental Studies / Observational study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: 40706