ArticleJournal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada2024
A National Discussion of COVID-19 on Twitter.
Article in Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Introduction: During the COVID-19 pandemic, social media became increasingly relied upon for health information in Canada. By analyzing georeferenced tweets using natural language processing, we aimed to understand regional discussions and concerns about school closures, masking, vaccines, and lockdowns during the pandemic's first two years. Methods: Using Twitter's application programming interface, we collected English-language tweets with keywords related to COVID-19 posted between January 1, 2020 and February 22, 2022 from Canadian users. Results: Out of all retained tweets, 2,851,951 (47.9%) were about vaccines, 1,344,008 (22.6%) about lockdowns, 1,011,909 (17%) about schooling, and 752,014 (12.6%) about masking. Tweets on schooling received the most engagement, with the highest rates of likes (17.3%), retweets (18.7%), replies (10%), and quotes (6.8%). The most common emotions expressed were trust, fear, and anticipation, with lockdown tweets showing greater fear and sadness. Overall, sentiment was negative, particularly regarding lockdowns in the Northwest Territories and Alberta. Discussion: During the COVID-19 pandemic, Twitter became an essential tool for analyzing public sentiment regarding government actions. Users showed the most interest in vaccines, followed by lockdowns, schooling, and masking, with the highest engagement on schooling tweets. Our analysis of sentiment, emotion, and content revealed valuable insights into public beliefs about COVID-19 in Canada, highlighting regional differences and shifts in sentiment, particularly negative reactions to school closures as government recommendations evolved. Our study adds to the growing evidence supporting the use of natural language processing for real-time analysis of social media content to early identify public health concerns.
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