Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic.
Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic.
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DOI：
10.1093/jamia/ocab047
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发表时间：
2021-07-14
期刊：
影响因子：
--
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
He L;He C;Reynolds TL;Bai Q;Huang Y;Li C;Zheng K;Chen Y
口罩是抗击新冠肺炎(冠状病毒病)大流行的重要个人防护措施。然而，口罩在美国的采用率仍然不是最理想的。这项研究旨在了解反对使用口罩的个人持有的信念，以及他们用来支持这些信念的证据，以指导有针对性的公共卫生传播战略的制定。我们分析了2020年1月至10月期间总共771至268条美国推文。我们开发了机器学习分类器来识别和分类相关的推文，然后对推文的子集进行定性的内容分析，以了解那些反对戴口罩的人的理由。我们识别了267条从152条推文中包含关于戴口罩以防止新冠肺炎传播的个人意见。虽然大多数推文支持戴口罩，但在整个研究期间，反对口罩的推文比例保持在10%左右。反对的常见原因包括身体不适和负面影响，缺乏效力，以及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外部信息来源，如公共卫生机构的网站来支持论点。将机器学习和定性内容分析相结合，是识别公众对戴口罩的态度和反对原因的有效策略。这一结果可能会为更好的沟通策略提供参考，以改善公众对戴口罩的感知，特别是具体解决常见的反口罩信念。
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.
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DOI：
10.1093/jamia/ocaa298
发表时间：
2021-06-01
影响因子：
6.4
作者：
He, Lu;Yin, Tingjue;Zheng, Kai
通讯作者：
Zheng, Kai
影响因子：
7.4
作者：
Kim Y;Huang J;Emery S
通讯作者：
Emery S
影响因子：
7.4
作者：
Davis MA;Zheng K;Liu Y;Levy H
通讯作者：
Levy H
影响因子：
8.3
作者：
French, P. Edward
通讯作者：
French, P. Edward
影响因子：
39.2
作者：
Bundgaard, Henning;Bundgaard, Johan Skov;Iversen, Kasper
通讯作者：
Iversen, Kasper