Author:
DeVerna Matthew R.,Pierri Francesco,Truong Bao Tran,Bollenbacher John,Axelrod David,Loynes Niklas,Torres-Lugo Christopher,Yang Kai-Cheng,Menczer Filippo,Bryden John
Abstract
With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about vaccines spreading on social media. In this paper, we present the CoVaxxy dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. Using one week of data, we provide statistics regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also illustrate how these data might be utilized by performing an analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. Additionally, we develop and present the CoVaxxy dashboard, allowing people to visualize the relationship between COVID-19 vaccine adoption and U.S. geo-located posts in our dataset. This dataset can be used to study the impact of online information on COVID-19 health outcomes (e.g., vaccine uptake) and our dashboard can help with exploration of the data.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
31 articles.
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