Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets

COVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the w...

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Main Authors: Jwen Fai Low, Benjamin C. M. Fung, Farkhund Iqbal
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10192425/
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author Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
author_facet Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
author_sort Jwen Fai Low
collection DOAJ
description COVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, and controversiality and disagreeableness, represented by ratios of the aforementioned counts, favored pro-mask tweets initially but with anti-mask tweets slowly gaining ground. Additional analysis raised the possibility of the platform owners suppressing certain parts of the mask-wearing discussion.
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spelling doaj.art-3004b4d574eb4bdaa6532a803f406db22023-08-21T23:00:27ZengIEEEIEEE Access2169-35362023-01-0111870098703310.1109/ACCESS.2023.329855710192425Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing TweetsJwen Fai Low0Benjamin C. M. Fung1https://orcid.org/0000-0001-8423-2906Farkhund Iqbal2https://orcid.org/0000-0001-9081-3598School of Information Studies, McGill University, Montreal, QC, CanadaSchool of Information Studies, McGill University, Montreal, QC, CanadaCollege of Technological Innovation, Zayed University, Abu Dhabi, United Arab EmiratesCOVID-19 is an opportunity to study public acceptance of a “new” healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, and controversiality and disagreeableness, represented by ratios of the aforementioned counts, favored pro-mask tweets initially but with anti-mask tweets slowly gaining ground. Additional analysis raised the possibility of the platform owners suppressing certain parts of the mask-wearing discussion.https://ieeexplore.ieee.org/document/10192425/Social mediaTwittercensorshipinformation diffusionratiometricsstance classification
spellingShingle Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
IEEE Access
Social media
Twitter
censorship
information diffusion
ratiometrics
stance classification
title Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
title_full Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
title_fullStr Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
title_full_unstemmed Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
title_short Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets
title_sort of stances themes and anomalies in covid 19 mask wearing tweets
topic Social media
Twitter
censorship
information diffusion
ratiometrics
stance classification
url https://ieeexplore.ieee.org/document/10192425/
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