Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework

Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework

Mohammad Daradkeh
Copyright: © 2022 |Pages: 22
DOI: 10.4018/IJBAN.292056
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Abstract

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.
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1. Introduction

The rapid spread of COVID-19 has caused unprecedented health, social and economic crises worldwide (Andersen, Rambaut, Lipkin, Holmes, & Garry, 2020). The World Health Organization (WHO) officially declared the outbreak a pandemic on March 11, 2020, and the development of COVID-19 vaccines has been a major undertaking for countries and international organizations to combat the spread of this pandemic (Loomba, de Figueiredo, Piatek, de Graaf, & Larson, 2021). As of December 2020, many vaccine candidates have been shown to be effective and protective in eliciting an immune response (Mulligan et al., 2020), with preliminary analyses of phase III trials demonstrating efficacy of up to 95% (Al-Qerem & Jarab, 2021; Jackson et al., 2020). However, ongoing efforts to achieve universal vaccination have been met with a fierce and devastating onslaught of fake news and misinformation about the importance, safety, or efficacy of vaccines (Guidry et al., 2021; Loomba et al., 2021; Praveen, Ittamalla, & Deepak, 2021). While part of this misinformation is simply confusing, much of it poses serious risks and harms vaccine uptake and acceptance (Wong et al., 2021). Thus, addressing the ongoing wave of misinformation about the COVID-19 vaccine and developing effective strategies to alleviate its impact is of paramount importance, especially as the news cycle, dominated by the unmediated spread of misinformation, is dramatically changing the nature of how people consume and report information (Ferrara, Cresci, & Luceri, 2020; Gupta & Aluvalu, 2021; Gupta, Ramadevi, Agarwal, & Shekhar Yadav, 2021).

Misinformation is often driven by highly networked modalities in which fake news, rumors, and misleading, fabricated conspiracy theories are generated, circulated, and broadly ingested through social media venues and other outlets (Al-Zaman, 2021; Islam et al., 2020). Since the outbreak of the COVID-19 pandemic, social media platforms such as Facebook, Twitter, and Instagram have become an important tool for governments and public health organizations to disseminate important information and raise public awareness about COVID-19 vaccination campaigns (Islam et al., 2020; Loomba et al., 2021). At the same time, social media is rapidly becoming the primary channel for public expression of opinion, classification of perceptions, and expression of skepticism about the vaccine and its effectiveness. While social media platforms provide immediate access to an unlimited amount of content, they also reinforce myths, fake news, and misinformation (Cinelli et al., 2020; Wani, Agarwal, & Bours, 2021). In particular, posts quickly circulate on social media claiming that vaccines are unnecessary or even harmful (Al-Zaman, 2021; Loomba et al., 2021; Praveen et al., 2021). This negative sentiment toward vaccination can influence vaccination readiness and thus lead to a decline in vaccination coverage rates (Islam et al., 2020; Marcec, Majta, & Likic, 2021; Wong et al., 2021). Population vaccination propensity is generally not static, but responds strongly to new evidence and sentiment related to COVID-19 vaccination, as well as the perceived risk of contracting the disease (Jose et al., 2021; van der Linden, Dixon, Clarke, & Cook, 2021). Therefore, to maintain vaccination coverage beyond the threshold of herd immunity, it is necessary to monitor current public concerns and sentiments about the COVID-19 vaccine and identify factors that might lead people to change their views about vaccines and their effectiveness (Chan, Jamieson, & Albarracin, 2020; Loomba et al., 2021).

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