Abstract
The Covid-19 pandemic has had a deep impact on the lives of the entire world population, inducing a participated societal debate. As in other contexts, the debate has been the subject of several d/misinformation campaigns; in a quite unprecedented fashion, however, the presence of false information has seriously put at risk the public health. In this sense, detecting the presence of malicious narratives and identifying the kinds of users that are more prone to spread them represent the first step to limit the persistence of the former ones. In the present paper we analyse the semantic network observed on Twitter during the first Italian lockdown (induced by the hashtags contained in approximately 1.5 millions tweets published between the 23rd of March 2020 and the 23rd of April 2020) and study the extent to which various discursive communities are exposed to d/misinformation arguments. As observed in other studies, the recovered discursive communities largely overlap with traditional political parties, even if the debated topics concern different facets of the management of the pandemic. Although the themes directly related to d/misinformation are a minority of those discussed within our semantic networks, their popularity is unevenly distributed among the various discursive communities.
Highlights
The Covid-19 pandemic has had a deep impact on nearly every human activity; as such, it is not surprising that it has generated a widespread online debate which, in turn, attracted the interest of scholars from different disciplines [1,2,3,4,5,6,7]
In the present paper we analyse the semantic network observed on Twitter during the first Italian lockdown and study the extent to which various discursive communities are exposed to d/misinformation arguments
2 Results 2.1 Identification of the discursive communities Users can interact on Twitter in different ways: for example, one can retweet the content of another user, endorsing it [43] and raising the content visibility; in order to infer the membership of the various accounts, in the present paper we leverage on this activity, following the procedure adopted in [17, 18]
Summary
The Covid-19 pandemic has had a deep impact on nearly every human activity; as such, it is not surprising that it has generated a widespread online debate which, in turn, attracted the interest of scholars from different disciplines [1,2,3,4,5,6,7]. Due to its unmediated nature, the online debate was affected by quite an amount of low-quality contents that, more than in other circumstances, had the potential to severely put at risk the public health In this sense, the High Representative of the Union for Foreign Affairs and Security Policy explicitly exposed her concerns about the possible effects of a wrong communication, on social media, during the pandemic: “Disinformation can have severe consequences: it can lead people to ignore official health advice and engage in risky behaviour, or have a negative impact on our democratic institutions, societies, as well as on our economic and financial situation.” [8]. Gallotti et al [3] released a real-time dashboard to monitor the risk of exposure to d/misinformation in the various countries on Twitter and proposed an Infodemic Risk Index, based on epidemic studies They were able to detect early-warning signals related to the diffusion of d/misinformation campaigns. Following a similar line of research, Yang et al [5] analysed both Twitter and Facebook, focusing their attention on links to low-credibility contents; in particular, they observed the presence of a limited number of extremely influential accounts, i.e. the d/misinformation super-spreaders
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
