Characterizing networks of propaganda on twitter: a case study
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Applied Network Science
RESEARCH
Open Access
Characterizing networks of propaganda on twitter: a case study Stefano Guarino1*
, Noemi Trino2 , Alessandro Celestini1 , Alessandro Chessa2,3 and Gianni Riotta2
*Correspondence: [email protected] 1 Institute for Applied Mathematics, National Research Council, Rome, Italy Full list of author information is available at the end of the article
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. Keywords: Propaganda networks, Polarization, Centrality, Clustering
Introduction The 2016 US presidential election veritably marked the transition from an age of ‘post-trust’ (Löfstedt 2005), to an era of ‘post-truth’ (Higgins 2016), with contemporary advanced democracies experiencing a rise of anti-scientific thinking and reactionary obscurantism, ranging from online conspiracy theories to the much-discussed “death of expertise” (Nichols 2017). The long-standing debate about the relationship between media and public good has been reinvigorated: the initial euphoria about the “openness” of the Internet (Lévy 2002) has been taken over by a widespread concern that social media may instead be undermining the quality of democracy (Tucker et al. 2018). Media outlets, public officials and activists are supplying citizens with different, often contradictory “alternative facts” (Allcott and Gentzkow 2017). In this context, social media platforms © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, ada
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