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Social media has become increasingly popular over the past two decades, with current statistics indicating that 2.65 billion people worldwide use at least one social media platform (Clement, 2019a). One popular platform is the microblogging site Twitter, which allows users to disseminate short messages to other users. Twitter has approximately 330 million active users, including 1 in 5 adults in the United States (Clement, 2019b; Hughes & Wojcik, 2019). According to a 2019 survey, 71% of Twitter users get news from the site and 42% use the site to discuss politics (Hughes & Wojcik, 2019). Twitter has also become a popular site for sharing and discussing science and science policy (Anderson & Huntington, 2017; Su et al., 2017). In the context of the COVID-19 pandemic, a number of major health organizations, such as the Center for Disease Control and Prevention and the World Health Organization, have launched social media campaigns to communicate with the general public about the nature of the novel coronavirus, current numbers of cases and deaths, public health recommendations, and other pertinent information (Merchant & Lurie, 2020). Due to the rapidly developing nature of the pandemic and widespread recommendations to reduce in-person social contacts, social media sites such as Twitter have also become an attractive means of sourcing and sharing pandemic-related information among the general public (Limaye et al., 2020).
Recent research has raised concern about polarization on Twitter, a phenomenon that occurs when users primarily interact with others who share their views within insular communities (Conover et al., 2011). Polarization produces social networks of users who share similar views and who are more likely to interact with one another than with other users. These networks are distinct from formal online groups in that they form organically through users’ online behaviors (e.g., engagement with others’ posts) and lack a defined structure or organization. Polarization can contribute to a number of negative outcomes, including the proliferation of false information and resistance to outside influence. A swell of misinformation (i.e., false information) related to COVID-19, including conspiracy theories, has already been documented on Twitter (Kouzy et al., 2020), raising concerns that this may be dissuading people from following the recommendations of health experts (Limaye et al., 2020). At the same time, there is a dearth of research on social networks that share COVID-related misinformation on Twitter. The overarching aim of the current study was to examine the characteristics of these networks using social network and sentiment analyses.