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Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election

Authors :
Bruno, Matteo
Lambiotte, Renaud
Saracco, Fabio
Source :
EPJ Data Sci. 11, 17 (2022)
Publication Year :
2021

Abstract

Online Social Networks represent a novel opportunity for political campaigns, revolutionising the paradigm of political communication. Nevertheless, many studies uncovered the presence of d/misinformation campaigns or of malicious activities by genuine or automated users, putting at severe risk the credibility of online platforms. This phenomenon is particularly evident during crucial political events, as political elections. In the present paper, we provide a comprehensive description of the structure of the networks of interactions among users and bots during the UK elections of 2019. In particular, we focus on the polarised discussion about Brexit on Twitter analysing a data set made of more than 10 million tweets posted for over a month. We found that the presence of automated accounts fostered the debate particularly in the days before the UK national elections, in which we find a steep increase of bots in the discussion; in the days after the election day, their incidence returned to values similar to the ones observed few weeks before the elections. On the other hand, we found that the number of suspended users (i.e. accounts that were removed by the platform for some violation of the Twitter policy) remained constant until the election day, after which it reached significantly higher values. Remarkably, after the TV debate between Boris Johnson and Jeremy Corbyn, we observed the injection of a large number of novel bots whose behaviour is markedly different from that of pre-existing ones. Finally, we explored the bots' stance, finding that their activity is spread across the whole political spectrum, although in different proportions, and we studied the different usage of hashtags by automated accounts and suspended users, thus targeting the formation of common narratives in different sides of the debate.<br />Comment: 18 pages, 13 figures

Details

Database :
arXiv
Journal :
EPJ Data Sci. 11, 17 (2022)
Publication Type :
Report
Accession number :
edsarx.2107.14155
Document Type :
Working Paper
Full Text :
https://doi.org/10.1140/epjds/s13688-022-00330-0