The Impact of Polarised Social Media Networking Communications in the #Longcovid Debate between Ideologies and Scientific Facts

A Language Analysis from a Corpus of Posts on Twitter/X

Abstract

The Covid-19 pandemic has shaped many conventions in social, cultural and linguistic terms. Its outbreak in early 2020 sparked an unprecedented R&D effort (Pedrini 2021; Burgess et al. 2021) by the international scientific community in developing effective vaccines (Knoll and Wonodi 2020). Indeed, these vaccines helped the world to return to normal life, though the vaccines’ effectiveness was questioned (Andrews et al. 2022).

The global debate concerning Covid-19 issues has not come to an end, especially in online and digital environments. Media play roles in creating and shaping representation(s) and truth(s) (Garfin et al. 2020; Chaiuk and Dunaievska 2020), but it is on social media networking platforms that discussions have been leading to patterns of polarisation. A case in point is Long Covid, or post-Covid-19 condition, defined “as a variety of mid- and long-term effects after [people] recover from their initial illness” (WHO 2021). Online discussion about this topic has increased over time.

The aim of this study is to present the results of an analysis of a corpus of automatically-retrieved (Brooker, Barnett and Cribbin 2016) social media networking content from Twitter (now called “X”) in a 13-week timespan (September 2022–November 2022) and made up of almost 600,000 tweets. The 2.5M-token corpus of tweets is investigated using quantitative and qualitative approaches (Stefanowitsch 2020) to retrieve data on users’ reactions about Long Covid. Some of these statements reflect conspiracy-based theories involving vaccination, fake news and post-truths, clashing with scientific evidence. Other tweets reflect supportive stances, thus leading to forms of ambient affiliation (Zappavigna 2011).