Facts or Followers? Identifying Key Variables in Medical-related Social Networking Sites and Computer-mediated Communication. A Case Study on Covid-19 Tweets

Computer-mediated communication (CMC) in digital and online environments has become a common practice that involves a growing community on a global scale (Herring and Androutsopoulos, 2015), encompassing discourse-related features in a language-oriented perspective (Leppänen 2017; Bouvier 2018; KhosraviNik 2023). In particular, Social Networking Sites (SNSs) have provided an unrivalled opportunity to communicate in a twofold direction: on the one hand, people can get information about a specific topic by retrieving both real-time and archived data; likewise, users are given a personal space to share evidence from often reliable sources, but also to express their own (and unreliable) views. A possible consequence is the rapid spread of fake news that appeal to an alleged sense of truthfulness (D’Ancona 2018), thus overcoming the range of facts spread via institutional sources, the latter being often attacked by users who undermine the concept of affiliation on the basis of mutual interests (Author, in press). The recent Covid-19 pandemic proved to be an infodemic, too (WHO 2021) and is still far from being over, at least in language and discourse-related terms. Both facts and unverified news are still making the news and are still part of SNS interactions, thus shaping the ideological views of people and users. Engagement on such platforms relies massively on the following/follower criterion, thus creating paths of information channelling depending on quantitative metrics and the impact of social “influence” provided by some digital opinion leaders (Locatelli 2020). This paper aims at providing a case study involving a sampling of random Covid-19 interactions on Twitter (now rebranded as X) over a short timespan. Following a resurgence of Covid-19-related interest due to new revelations concerning the origin of the pandemic and in a one-month timespan (March 2023-April 2023), the hypothesis is that some messages appear to be more influential and get more engagement irrespectively of their intrinsic truth but on the basis of the influential user spreading such information. As a result, the popularisation of medical facts may be dramatically hindered by non-objective processes and be preferred to scientific methods. Using a specific retrieval tool (Tweetcatcher, Brooker et al. 2016), tweets are randomly collected, arranged and analysed on the basis of device-specific metrics (e.g. number of following/followers, number of replies) to assess their engagement in relation to their potentially harmful, non-factual meaning.