Source of Publication
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.
Institute of Electrical and Electronics Engineers (IEEE)
Blogs, censorship, Classification algorithms, COVID-19, information diffusion, Information exchange, Machine learning, machine learning, Pandemics, Radiometers, ratiometrics, social media, Social networking (online), stance classification, Standards, summarization, theme classification, transformers, Transformers, Twitter, Vaccines
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Low, Jwen Fai; Fung, Benjamin C.M.; Iqbal, Farkhund; and Bagheri, Ebrahim, "Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets" (2023). All Works. 5967.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series