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Measuring user engagement with low credibility media sources in a controversial online debate.
Vilella, Salvatore; Semeraro, Alfonso; Paolotti, Daniela; Ruffo, Giancarlo.
  • Vilella S; Department of Computer Science, University of Turin, Turin, Italy.
  • Semeraro A; Department of Computer Science, University of Turin, Turin, Italy.
  • Paolotti D; ISI Foundation, Turin, Italy.
  • Ruffo G; Department of Computer Science, University of Turin, Turin, Italy.
EPJ Data Sci ; 11(1): 29, 2022.
Article in English | MEDLINE | ID: covidwho-1874692
ABSTRACT
We quantify social media user engagement with low-credibility online news media sources using a simple and intuitive methodology, that we showcase with an empirical case study of the Twitter debate on immigration in Italy. By assigning the Twitter users an Untrustworthiness (U) score based on how frequently they engage with unreliable media outlets and cross-checking it with a qualitative political annotation of the communities, we show that such information consumption is not equally distributed across the Twitter users. Indeed, we identify clusters characterised by a very high presence of accounts that frequently share content from less reliable news sources. The users with high U are more keen to interact with bot-like accounts that tend to inject more unreliable content into the network and to retweet that content. Thus, our methodology applied to this real-world network provides evidence, in an easy and straightforward way, that there is strong interplay between accounts that display higher bot-like activity and users more focused on news from unreliable sources and that this influences the diffusion of this information across the network.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Qualitative research / Randomized controlled trials Language: English Journal: EPJ Data Sci Year: 2022 Document Type: Article Affiliation country: Epjds

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Qualitative research / Randomized controlled trials Language: English Journal: EPJ Data Sci Year: 2022 Document Type: Article Affiliation country: Epjds