Flow of online misinformation during the peak of the COVID-19 pandemic in Italy

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but,...

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Vydáno v:EPJ data science Ročník 10; číslo 1; s. 34 - 23
Hlavní autoři: Caldarelli, Guido, De Nicola, Rocco, Petrocchi, Marinella, Pratelli, Manuel, Saracco, Fabio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 06.07.2021
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
COVID-19
COVID-19 Infodemic
Data science
Data-driven Science
Disease transmission
Effectiveness
Emergency response
Entropy
False information
Human influences
Interest groups
Misinformation
Modeling and Theory Building
Pandemics
Politics
Public health
Random noise
Regular
Regular Article
Social networks
Social organization
Twitter
Italy
Twitter
Misinformation
COVID-19 Infodemic
ISSN:2193-1127, 2193-1127
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Shrnutí:The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.
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ISSN:2193-1127
2193-1127
DOI:10.1140/epjds/s13688-021-00289-4