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

Author:

He Lu1ORCID,He Changyang2,Reynolds Tera L1,Bai Qiushi3,Huang Yicong3,Li Chen3,Zheng Kai14,Chen Yunan1

Affiliation:

1. Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA

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, USA

4. Department of Emergency Medicine, School of Medicine, University of California, Irvine, Irvine, California, USA

Abstract

Abstract Objective Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States 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 U.S.-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 a 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. Conclusions 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.

Funder

National Center for Research Resources

National Center for Advancing Translational Sciences

National Institutes of Health

Orange County Health Care Agency

NSF RAPID award

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference48 articles.

1. Effectiveness of adding a mask recommendation to other public health measures to prevent SARS-CoV-2 infection in Danish mask wearers;Bundgaard;Ann Intern Med,2020

2. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic;Eikenberry;Infect Dis Model,2020

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