Detecting Political Bots on Twitter during the 2019 Finnish Parliamentary Election

Rossi, Sippo; Rossi, Matti; Upreti, Bikesh Raj; Liu, Yong

doi:10.24251/hicss.2020.298

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…Among other usages, it was showed that their employment may foster, precisely, the formation of echo chambers [ 19 ] or increase the visibility and credibility of users [ 6 , 8 , 20 , 21 ]. The injection of coordinated accounts is particularly common during elections and key events in several countries [ 22 – 24 ]. Detecting the bots and analysing the behaviour of malicious accounts is not, however, an easy task.…”

Section: Introductionmentioning

confidence: 99%

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

Lambiotte

Saracco

2022

EPJ Data Sci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Lambiotte

Saracco

2022

EPJ Data Sci.

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“…erefore, identifying the behavioral patterns of social bots participating in social issue discussions has also become a research focus of scholars. Rossi et al [5] classified political bots into three types according to their complexity. e simplest bots are only used to increase the number of followers of an account or the number of tags/likes of a particular post, but these bots are easily monitored and deleted by the platforms; the second-most complex bots can dynamically adjust their content information by tracking the behavior of other target accounts; the most sophisticated bots are those in which humans control some of the bot's activities in terms of content creation, and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Information Diffusion Model of Social Bots: An Analysis of the Spread of Coverage of China Issues by The New York Times on Twitter

et al. 2022

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Social-bots-mediated information manipulation is influencing the public opinion environment, and their role and behavior patterns in news proliferation are worth exploring. Based on the analysis of bots' posting frequency, influence, and retweeting relationship, we take the diffusion of The New York Times' coverage of Xinjiang issue on the overseas social platform Twitter as an example and employ the two-step flow model. It is found that in the role of second-step diffusion, unlike posting news indiscriminately in first-step diffusion, social bots are more inclined to postcontroversial information in second-step diffusion; in terms of diffusion patterns, although social bots are more engaged in first-step diffusion than in second-step diffusion and can trigger human users to retweet, they are still inferior to humans in terms of influence.

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“…Besides the US elections a high bot activity with spreading of misleading information is also detected during election periods (presidential/parliamentary/state) in countries like Germany, Sweden, France, Spain and etc. (Neudert, Kollanyi, and Howard 2017;Pastor-Galindo et al 2020;Bradshaw et al 2017;Fernquist, Kaati, and Schroeder 2018;Castillo et al 2019;Rossi et al 2020). Furthermore, election discourse is not the only point of interest for bot account creators.…”

Section: Introductionmentioning

confidence: 99%

Discovery and Classification of Twitter Bots

et al. 2022

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Twitter as one of the most popular social networks, offers a means for communication and online discourse, which unfortunately has been the target of bots and fake accounts, leading to the manipulation and spreading of false information. Towards this end, we gather a challenging, multilingual dataset of social discourse on Twitter, originating from 9M users regarding the recent Russo-Ukrainian war, in order to detect the bot accounts and the conversation involving them. We collect the ground truth for our dataset through the Twitter API suspended accounts collection, containing approximately 343K of bot accounts and 8M of normal users. Additionally, we use a dataset provided by Botometer-V3 with 1,777 Varol, 483 German accounts, and 1,321 US accounts. Besides the publicly available datasets, we also manage to collect 2 independent datasets around popular discussion topics of the 2022 energy crisis and the 2022 conspiracy discussions. Both of the datasets were labeled according to the Twitter suspension mechanism. We build a novel ML model for bot detection using the state-of-the-art XGBoost model. We combine the model with a high volume of labeled tweets according to the Twitter suspension mechanism ground truth. This requires a limited set of profile features allowing labeling of the dataset in different time periods from the collection, as it is independent of the Twitter API. In comparison with Botometer our methodology achieves an average 11% higher ROC-AUC score over two real-case scenario datasets.

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Detecting Political Bots on Twitter during the 2019 Finnish Parliamentary Election

Cited by 19 publications

References 17 publications

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

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

Information Diffusion Model of Social Bots: An Analysis of the Spread of Coverage of China Issues by The New York Times on Twitter

Discovery and Classification of Twitter Bots

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