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A Look into COVID-19 Vaccination Debate on Twitter

Authors: Malagoli, Larissa G.; Stancioli, Júlia; Ferreira, Carlos H. G.; Couto da Silva, Ana Paula; Almeida, Jussara M.; Vasconcelos, Marisa;

A Look into COVID-19 Vaccination Debate on Twitter

Abstract

Our data collection was driven by the goal of gathering a corpus of English-language tweets that would be informative of the online debate on COVID-19 vaccines worldwide. To that end, we used the Twitter API Search to collect tweets based on specific keywords related to COVID-19 vaccination. We built a list of such keywords that include terms related to both pro and anti-vaccine discourse as well as words related to the most well known COVID-19 vaccines available so far. Specifically, we consider the following list of keywords: vaccine, vaccination, anti-vaccination, antivax, anti-vaccine, anti-vax, anti-vaxxers, NoForcedVaccination, getvaccinated, pfizer, moderna, astrazeneca, covaxin, biontech, novavax, coronavac, sputnikv, bnt162b2. In total, we gathered over $12$ million tweets, covering 9 weeks, from December 1st, 2020 to January 31st, 2021. This is an important period that includes the launch of the first worldwide COVID-19 vaccination campaign (launched on December 8th in the United Kingdom), as well as several other important real-world events that influenced and dictated people's discussions. This dataset is aggregated by weeks and keywords. Only the tweets IDs are available following Twitter's Privacy Policy. Please cite as: MALAGOLI, L. G. ; STANCIOLI, J. ; VASCONCELOS, M. ; FERREIRA, C. H. ; SILVA, A. P. C. ; ALMEIDA, Jussara. A Look into COVID-19 Vaccination Debate on Twitter. In: 13th ACM Conference on Web Science, 2021.

Keywords

Corona Virus, COVID-19, Vaccine, Twitter Dataset

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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