Twitter Bots and the Polarization of COVID-19 Vaccine

• In Event: Digital Information Flows and Politics in the COVID-19 Pandemic

Abstract

The Covid-19 vaccine was strongly politicized, with conservative media and Republican politicians often expressing more negative attitudes and distrust towards the vaccine, resulting in higher levels of misinformation and distrust among conservatives (Motta et al., 2020). These partisan gaps in perceptions resulted in dramatic differences in vaccine coverage between Trump and Biden supporters (Kates et al., 2021).
Among the spreaders of vaccine misinformation online are social bots. While some studies argued that overall bots express more pro-vaccine positions (Yuan et al., 2019) and thus have limited role in spreading anti-vaccine sentiment (Dunn et al., 2020), others found that social bots use a more negative tone in regard to vaccines compared to humans (Shi et al., 2020). Worryingly, studies found that bots promoted partisan and politicized discourse around vaccines (Walter et al., 2020). These communication strategies, often resulting in echo chambers and amplification, were found to be particularly influential, especially when employed by conservative bots (Luceri et al., 2019).
In light of these findings, and the rising concern over the politicization of vaccines and the rise of anti-vaccine sentiment associated with the conservative right and Republican counties and states,, this study examines the politicization of vaccines by Twitter bots during the outbreak of the Omicron variant in late 2021.Specifically, we will compare the linguistic nature of vaccine-related discourse by human and social bots during the Omicron outbreak, with a focus on politicized discourse, i.e., tweets about the vaccine that also mentioned Donald Trump, Joe Biden, and the Democratic and Republican parties.
Using the Academic Twitter API, we collected all tweets published between November 1st 2021 and December 31 2021, that included the terms “vaccine” and “Trump” or “Biden” or Republican” or “Democrat” (N = 2,128,819 tweets). We will use a mixed-method approach to analyze the data, combining unsupervised machine learning -- analysis of topic modeling networks (ANTMN, Walter & Ophir, 2019) to identify primary and specific themes of discourse used by human and social bots, and supplemented by a manual content analysis to evaluate the sentiment. We use the Botometer Pro API to identify bots in our data, and compare the levels of politicization of the Covid-19 vaccine among bots and humans. In other words, we will examine RQ1) whether bots spread more politicized discourse than humans, RQ2) whether such discourse was more positive or negative in sentiment towards the vaccines, and RQ3) whether bots promoted a specific political side in the debate. Practical and theoretical implications will be discussed.
References
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Authors

• Rui Wang, University at Buffalo

• Yotam Ophir, University at Buffalo

• Dror Walter, Georgia State University