Characterizing networks of propaganda on twitter: a case study

• Main Authors: Stefano Guarino, Noemi Trino, Alessandro Celestini, Alessandro Chessa, Gianni Riotta
• Format: Article
• Language: English
• Published: SpringerOpen 2020-09-01
• Series: Applied Network Science
• Subjects: Propaganda networks; Polarization; Centrality; Clustering
• Online Access: http://link.springer.com/article/10.1007/s41109-020-00286-y

Abstract The daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the “polarization” of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a “central” role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.