Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic. (24th April 2021)

• Record Type: Journal Article
• Authors: He, Lu; He, Changyang; Reynolds, Tera L; Bai, Qiushi; Huang, Yicong; Li, Chen; Zheng, Kai; Chen, Yunan
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 7(2021)
• Page Start: 1564
• Page End: 1573
• Publication Date: 2021-04-24
• Subjects: 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]; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab047
• Languages: English
• ISSNs: 1067-5027

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.