Abstract
As more people use social media as a source of news and information, it is important to understand its impact on individual health decisions. This article compares the sentiment expressed in COVID-19 related tweets with national rates for first dose vaccinations as recorded by the Centers for Disease Control and Prevention. To conduct the study, the text from over 570,000 COVID-related tweets from January 2021 to December 2021 was captured. The tweets were segregated by month and Google Cloud’s Natural Language API was used determine the sentiment in each tweet, with each post labeled as having positive, negative, or neutral sentiment. Overall, there was greater prevalence of negative sentiment as compared with positive sentiment during the period of review, with 45% of tweets negative, 33% positive and 22% neutral. The number of positive and negative tweets was more balanced in the early months of 2021 (when the vaccine was first available) and became decidedly more negative in the later part of the year, as misinformation about the vaccines spread prolifically on social media. This comparison of the tweet sentiment to first-time vaccine doses in the US shows that misinformation about vaccines on social media appears to have had an impact on behavior. Vaccine adoption declined significantly in the latter half of 2021, even as vaccines and information from public health officials regarding their efficacy became more available to the general public. These findings are validated by subsequent analysis of word usage by month, with positive comments about vaccines and vaccination in January through May coinciding with high vaccination rates, and a negative conversational shift to variants, increased deaths and suspicion about vaccine safety and effectiveness later in the year during a stagnation period in vaccinations.
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Cooper, A., Danforth, M., Edwards, A. (2022). Comparing Twitter Sentiment and United States COVID-19 Vaccination Rates. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_2
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DOI: https://doi.org/10.1007/978-3-031-19682-9_2
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