Title: Affective Self-positioning and COVID-19 Vaccine Sentiment on Twitter: From Fear and Hesitancy to Solidarity and Trust
Stream: Cultural and Media Studies
Presentation Type: Live-Stream Presentation
Authors:
Jana Sverdljuk, University of Agder, Norway
Bastiaan Bruinsma, Chalmers University of Technology in Gothenburg, Sweden
Abstract:
During the COVID-19 pandemic, vaccine narratives proliferated on social media. In the period from January 2020 to August 2021, there were more than 50 million tweets dealing with vaccine. There were problematic narratives and discourses considered by the UN and WHO as obstacles to overcoming the pandemic. For example, anti-vaxxers were criticizing Western pharmaceutical industry and spreading fake news (Burki 2020). They were encouraged by influencers (such as Donald J. Trump), had a strong sense of community, and used highly emotional language (Germani & Biller-Andorno 2021). At the same time, many politicians and governments pursued narrow national interests with regards to vaccine distribution showing little concern about global justice. Apart from these negative narratives, there were examples of international solidarity and trust to medical institutions; many users were posting their inspiring stories of recovering and advocating vaccination (Rosenberg et. al. 2020). All in all, emotions played an important role in advancing various positions, and each position implied affective, pre-reflexive attitudes towards vaccine and its impact on the body. Using the material of 50 million tweets on “vaccine”, the presentation identifies the most re-tweeted content and most influential users (with the greatest number of followers). What is the subjective, emotional dynamics of how these users adopt various positions on vaccine? What affects legitimize their positions? Can one see the tendencies towards vaccine acceptance and global solidarity? The main analytical approaches are the theory on “affective-discursive practice”, (Wetherell 2012), as well as Python-based data processing and sentiment analysis (Gao & Sebastiani 2016).
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