Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
J Am Med Inform Assoc. 2021 Jul 14;28(7):1564-1573. doi: 10.1093/jamia/ocab047.
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.
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.
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.
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.
口罩是抗击 COVID-19（冠状病毒病）大流行的重要个人防护措施。然而，美国的口罩佩戴率仍然不尽如人意。本研究旨在了解反对佩戴口罩的个人所持有的信念，以及他们用来支持这些信念的证据，以为制定有针对性的公共卫生传播策略提供信息。
我们分析了 2020 年 1 月至 10 月期间美国的共计 771,268 条推文。我们开发了机器学习分类器来识别和分类相关推文，然后对其中一部分推文进行定性内容分析，以了解反对戴口罩者的基本原理。
我们确定了 267,152 条推文，其中包含了关于戴口罩预防 COVID-19 传播的个人意见。虽然大多数推文支持戴口罩，但在整个研究期间，反口罩推文的比例一直保持在 10%左右。反对的常见原因包括身体不适和负面影响、缺乏有效性，以及在某些情况下对某些人或在某些情况下不必要或不合适。反对的推文明显不太可能引用公共卫生机构网站等外部信息来源来支持这些论点。
结合使用机器学习和定性内容分析是识别公众对戴口罩态度和反对原因的有效策略。研究结果可能为改善公众对戴口罩的看法提供信息，特别是针对常见的反口罩信念提供信息。
