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      Why do people oppose mask wearing? A comprehensive analysis of US tweets during the COVID-19 pandemic

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          Abstract

          Objective

          Facial masks are an essential personal protective measure to fight the COVID-19 pandemic. However, the mask adoption rate in the US is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies.

          Materials and Methods

          We analyzed a total of 771,268 US-based tweets between January to October 2020. We developed machine-learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing.

          Results

          We identified 267,152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies’ websites to support the arguments.

          Discussion and Conclusion

          Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

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          Author and article information

          Journal
          J Am Med Inform Assoc
          J Am Med Inform Assoc
          jamia
          Journal of the American Medical Informatics Association : JAMIA
          Oxford University Press
          1067-5027
          1527-974X
          04 March 2021
          : ocab047
          Affiliations
          [1 ]Department of Informatics, Donald Bren School of Information and Computer Science, University of California , Irvine, Irvine, California, United States
          [2 ]Department of Computer Science and Engineering, Hong Kong University of Science and Technology , Hong Kong SAR, China
          [3 ]Department of Computer Science, Donald Bren School of Information and Computer Science, University of California , Irvine, Irvine, California, United States
          [4 ]Department of Emergency Medicine, School of Medicine, University of California , Irvine, Irvine, California, United States
          Author notes

          Denotes equal contribution

          Corresponding author: Yunan Chen, PhD, MBBS, 6099 Bren Hall, Irvine, CA 92697-3440, United States. yunanc@ 123456ics.uci.edu , (949) 824-0959
          Article
          ocab047
          10.1093/jamia/ocab047
          7989302
          33690794
          b5b4297f-d3ba-47b6-b1ef-eb533c033fb3
          © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com

          This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

          This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

          History
          : 31 December 2020
          : 02 March 2021
          : 27 February 2021
          Page count
          Pages: 33
          Categories
          Research and Applications
          AcademicSubjects/MED00580
          AcademicSubjects/SCI01060
          AcademicSubjects/SCI01530
          Custom metadata
          accepted-manuscript
          PAP

          Bioinformatics & Computational biology
          social media [l01.178.751],natural language processing [l01.224.050.375.580],machine learning [g17.035.250.500],public health [h02.403.720],health communication [l01.143.350],coronavirus [b04.820.504.540.150],masks [e07.325.877.500],personal protective equipment [e07.700.560]

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