Characterizing polarization in online vaccine discourse-A large-scale study.

Bjarke Mønsted,Sune Lehmann,Christopher M Danforth

doi:10.1371/journal.pone.0263746

Bjarke Mønsted, Sune Lehmann + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0263746

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Abstract

Vaccine hesitancy is currently recognized by the WHO as a major threat to global health. Recently, especially during the COVID-19 pandemic, there has been a growing interest in the role of social media in the propagation of false information and fringe narratives regarding vaccination. Using a sample of approximately 60 billion tweets, we conduct a large-scale analysis of the vaccine discourse on Twitter. We use methods from deep learning and transfer learning to estimate the vaccine sentiments expressed in tweets, then categorize individual-level user attitude towards vaccines. Drawing on an interaction graph representing mutual interactions between users, we analyze the interplay between vaccine stances, interaction network, and the information sources shared by users in vaccine-related contexts. We find that strongly anti-vaccine users frequently share content from sources of a commercial nature; typically sources which sell alternative health products for profit. An interesting aspect of this finding is that concerns regarding commercial conflicts of interests are often cited as one of the major factors in vaccine hesitancy. Further, we show that the debate is highly polarized, in the sense that users with similar stances on vaccination interact preferentially with one another. Extending this insight, we provide evidence of an epistemic echo chamber effect, where users are exposed to highly dissimilar sources of vaccine information, depending the vaccination stance of their contacts. Our findings highlight the importance of understanding and addressing vaccine mis- and dis-information in the context in which they are disseminated in social networks.

Highlights

Vaccine hesitancy, defined as the reluctance or refusal to vaccinate [1], is a growing threat to global health, and is believed to be driven mainly by the ‘three C’s’: Confidence, Complacency, and Convenience [2]
Our findings paint a picture of the vaccine discourse on Twitter as highly polarized, where users who express similar sentiments regarding vaccinations are more likely to interact with one another, and tend to share contents from similar sources
Focusing on users whose vaccination stances are the positive and negative extremes of the spectrum, we observe relatively disjoint ‘epistemic echo chambers’ which imply that members of the two groups of users rarely interact, and in which users experience highly dissimilar ‘information landscapes’ depending on their stance

Summary

Introduction

Vaccine hesitancy, defined as the reluctance or refusal to vaccinate [1], is a growing threat to global health, and is believed to be driven mainly by the ‘three C’s’: Confidence, Complacency, and Convenience [2]. Correlations between the mean vaccine sentiment expressed in neighboring users’ tweets were roughly the same independently of how frequently the users interacted, as shown, the number of users in the interaction graph decreases quickly when using strict inclusion criteria (additional analyses in the methods section). The finding that users interact disproportionally with other users sharing their stance, aligns with previous findings that long time anti-vaccine users of social media tend to form tightly knit clusters which exhibit a high degree of in-group solidarity [46], and in which misinformation may thrive unquestioned [47] To qualify the latter, we turn again to the URLs most frequently shared by users discussing vaccines, shown, we probe regions in the MMR network around individuals of various stances and assess whether the URLs shared in those regions differ more or less from a normal distribution depending on stance. Some studies indicate that people are highly selective in sharing information that aligns well with their convictions [51], which in term can cause polarization by opinion reinforcement [52], and by users cutting ties to avoid exposure to information causing cognitive dissonance [53]

Discussion

Materials and methods

Findings

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