Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate

Salvatore Vilella, Mirko Lai, Daniela Paolotti and Giancarlo Ruffo
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Salvatore Vilella: Department of Computer Science, University of Turin, 10149 Torino, Italy
Mirko Lai: Department of Computer Science, University of Turin, 10149 Torino, Italy
Daniela Paolotti: ISI Foundation, 10126 Torino, Italy
Giancarlo Ruffo: Department of Computer Science, University of Turin, 10149 Torino, Italy

Future Internet, 2020, vol. 12, issue 10, 1-22

Abstract: In this work, we apply network science to analyse almost 6 M tweets about the debate around immigration in Italy, collected between 2018 and 2019, when many related events captured media outlets’ attention. Our aim was to better understand the dynamics underlying the interactions on social media on such a delicate and divisive topic, which are the actors that are leading the discussion, and whose messages have the highest chance to reach out the majority of the accounts that are following the debate. The debate on Twitter is represented with networks; we provide a characterisation of the main clusters by looking at the highest in-degree nodes in each one and by analysing the text of the tweets of all the users. We find a strongly segregated network which shows an explicit interplay with the Italian political and social landscape, that however seems to be disconnected from the actual geographical distribution and relocation of migrants. In addition, quite surprisingly, the influencers and political leaders that apparently lead the debate, do not necessarily belong to the clusters that include the majority of nodes: we find evidence of the existence of a ‘silent majority’ that is more connected to accounts who expose a more positive stance toward migrants, while leaders whose stance is negative attract apparently more attention. Finally, we see that the community structure clearly affects the diffusion of content (URLs) by identifying the presence of both local and global trends of diffusion, and that communities tend to display segregation regardless of their political and cultural background. In particular, we observe that messages that spread widely in the two largest clusters, whose most popular members are also notoriously at the opposite sides of the political spectrum, have a very low chance to get visibility into other clusters.

Keywords: network analysis; social media; network segregation; immigration; clusters; information cascades (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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