Your browser doesn't support javascript.
Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic.
He, Lu; He, Changyang; Reynolds, Tera L; Bai, Qiushi; Huang, Yicong; Li, Chen; Zheng, Kai; Chen, Yunan.
  • He L; Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • He C; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
  • Reynolds TL; Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • Bai Q; Department of Computer Science, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • Huang Y; Department of Computer Science, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • Li C; Department of Computer Science, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • Zheng K; Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, USA.
  • Chen Y; Department of Emergency Medicine, School of Medicine, University of California, Irvine, Irvine, California, USA.
J Am Med Inform Assoc ; 28(7): 1564-1573, 2021 07 14.
Article in English | MEDLINE | ID: covidwho-1123294
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Attitude to Health / Social Media / Machine Learning / COVID-19 / Masks Type of study: Qualitative research / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Attitude to Health / Social Media / Machine Learning / COVID-19 / Masks Type of study: Qualitative research / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia