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

作者：Loni Hagen, Ashley Fox, Heather O’Leary, DeAndre Dyson, Kimberly Walker, Cecile A. Lengacher, Raquel Hernandez · 发表于：JMIR Infodemiology · 年份：2022 · DOI：10.2196/34231 · 被引用次数：21 · 研究领域：Vaccine Coverage and Hesitancy、Misinformation and Its Impacts、SARS-CoV-2 and COVID-19 Research

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 exper...