4.7 Article

Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter

Journal

JOURNAL OF MEDICAL INTERNET RESEARCH
Volume 25, Issue -, Pages -

Publisher

JMIR PUBLICATIONS, INC
DOI: 10.2196/40706

Keywords

face masks; COVID-19; Twitter; science communication; political communication; public policy; public health; sentiment analysis; emotion analysis; infodemiology; infoveillance

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During the COVID-19 pandemic, there were fluctuating policies on face mask use by the US Centers for Disease Control and Prevention. Analyzing sentiment on masks surrounding guideline changes can inform future communication strategies and improve widespread and sustained adoption.
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 (beta=-3.94 percentage points, 95% CI -4.68 to -3.21; P<.001) and 2021 (beta=-8.74, 95% CI -9.31 to -8.17; P<.001). Following the April 3 recommendation (beta=.51, 95% CI .43-.59; P<.001) and May 13 relaxation (beta=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 (beta=-.004, 95% CI -.004 to -.003; P<.001) and May 13 (beta=-.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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