The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding

Abstract

Background: Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake.

Objective: The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy.

Methods: We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of "influentialness" of Twitter accounts and identifying the "influencers," followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities.

Results: Twitter vaccine conversations were highly polarized, with different actors occupying separate "clusters." The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with "trust" in vaccines being manipulated to the political advantage of partisan actors.

Conclusions: These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.

Keywords: COVID-19, vaccine hesitancy, social media, influential actors; Twitter; influencer.

Figure 1

Data collection and analysis.

Figure 2

Network graph of Twitter conversations about COVID 19 vaccines using the 1992 accounts with the highest PageRank; 2 clusters (explaining 3% and 0% of all the nodes) were excluded. Node color indicates a unique cluster, and node size indicates the level of influence (according to PageRank), with bigger nodes more influential among the networks.

Figure 3

K-core graphs demonstrating the density of groups: (A) 2-core, (B) 5-core, (C) 6-core, and (D) 7-core.