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

Abstract

Online Social Networks (OSNs) offer new means for political communications that have quickly begun to play crucial roles in political campaigns, due to their pervasiveness and communication speed. However, the OSN environment is quite slippery and hides potential risks: many studies presented evidence about the presence of d/misinformation campaigns and malicious activities by genuine or automated users, putting at severe risk the efficiency of online and offline political campaigns. This phenomenon is particularly evident during crucial political events, as political elections. In the present paper, we provide a comprehensive description 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 millions tweets posted for over a month. We found that the presence of automated accounts infected 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’ political orientation, finding that their activity is spread across the whole political spectrum, although in different proportions, and we studied the different usage of hashtags and URLs by automated accounts and suspended users, targeting the formation of common narratives in different sides of the debate.

Bow-tie structures of twitter discursive communities

“…As highlighted in other works 21 , 26 , 29 , 30 , 32 – 34 , the presence of well-defined discursive communities is the signal that users on Online Social Networks (OSNs) are strongly polarized, i.e., they tend to tend to split into groups, which one with same opinions and political orientation.…”

“…We identified the discursive communities in the retweet networks and we investigated the presence of bow-tie structures in such communities. In previous works, discursive communities were shown to mirror the political orientation of users 21 – 24 , 26 , 29 , 30 , 32 – 34 , thus the analysis of their structure is of utmost importance to infer the way opinions create and circulate.…”

“…The recent literature has extensively analysed online debate and discourse communities, focusing, from time to time, on coordinated activities in discursive communities 26 , 29 , 30 , on the semantic network associated to the various discursive communities 21 , 31 , 32 , on their exposure to disinformation campaigns 33 , 34 , and on their dynamical evolution 35 – 39 .…”

Quantifying the structure of controversial discussions with unsupervised methods: a look into the Twitter climate change conversation

“…Second, we do not distinguish between the nature of the Twitter users, so we did not analyze the different effects that validated accounts, non-human accounts, and bots may bring into the conversation. Previous large-scale studies of Twitter data have demonstrated the influence bots can have on the exposure of human accounts to emotional content [5], and how they can coordinate and distort the discussion in controversial conversations [13,8]. Thus, we could incorporate botdetection methods and create a taxonomy of users to analyze their effects.…”

Verified authors shape X/Twitter discursive communities

“…Recently, entropy-based null models were presented in the literature of Complex Networks (Cimini et al, 2019): they represent a tailored and, at the same time, unbiased tool for detecting the statistically significant signal in complex networks. In the case of social networks, they were implemented in order to uncover coordinated behaviours of social bots (Caldarelli et al, 2020;Bruno et al, 2022), different semantic networks (Radicioni et al, 2021a,b;Mattei et al, 2021), accounts displaying the same information diets (Guarino et al, 2021) and the formation of discursive communities and their structures (Becatti et al, 2019;Caldarelli et al, 2021;Mattei et al, 2022;De Clerck et al, 2022a,b;Pratelli et al, 2023). In Ref.…”