COVID-19 Vaccine Discussion: Evidence from Twitter Data Using Text Mining
Pages 90 - 96
Abstract
COVID-19 vaccination has led to unrest within societies, and intense public debates are often carried out on social media platforms like Twitter. A better understanding of concerns, issues, and communication on COVID-19 vaccines is a first step to reducing tension within society and improving the negative effects of the pandemic. It can also contribute to addressing the concerns of advocates and opponents, which is essential in the battle against this and possible future pandemics. At the same time, many people report pressure to undergo vaccination in order to continue participating in social and professional life. COVID-19 vaccination has triggered a complex discussion among the public. We use text mining algorithms suitable for big datasets to identify relevant categories of discourse and sentiments from about 250,000 tweets. Our findings highlight (and quantify) expressed shortcomings in vaccination programs related to administration, planning, information, and protective measures. It also hints that rare and severe incidents related to vaccination have a more substantial impact than potential fears related to non-familiar technology such as “mRNA” causing uncertainty. We also provide an extensive discussion setting forth suggestions that might help deal with the current and future pandemic.
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Published: 11 April 2022
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