Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum

By Philip N. Howard, Bence Kollanyi. arXiv.org, 2016

391 citations recorded by the source corpus.

Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/da9cb56b9c7c2c2b191dd658b1830774d741fc62

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Citations in the corpus

The Polarizing Effects of Online Partisan Criticism: Evidence from Two Experiments (2018, background): “…that many individuals and groups— ranging from subcultural groups to unethical politicians to foreign governments—take advantage of the relative anonymity of the online sphere to manipulate online conversation via “trolls” and “bots” (Chen 2016; Howard and Kollanyi 2016; Marwick and Lewis 2017).”
The Brexit Botnet and User-Generated Hyperpartisan News (2019, background): “Finally, Howard and Kollanyi (2016) claim that in the E.U. referendum bots were designed to take sides in the debate about the U.K.’s membership of the European Union.”
Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward (2021, methodology): “A study published in [22, 23] concludes that during the US election campaign-2016, bots were employed to generate one-fifth of the tweets during the last month of the campaign.”
Deep Neural Networks for Bot Detection (2018, methodology): “Reports of online manipulation mediated by bots span political conversation [6, 21, 31, 40, 59], fake news [19, 50], conspiracy theories [53], stock market manipulation [18], public health [12], and more [27] — it is worth noting that in some rare occasions bots have been used to deliver positive…”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, neutral): “Since then, an increasing account of such events has been recorded in the context of several elections, both in the United States (Bessi & Ferrara, 2016; Kollanyi, et al., 2016; Shorey & Howard, 2016; Wolley, 2016; Wolley & Howard, 2016; Marwick & Lewis, 2017; Wang, et al., 2017), and all over the w”
Finding and Characterizing Information Warfare Campaigns (2020, background): “Since recent articles and attention has been paid to state-sponsored use of bots in numerous events, particularly democratic events (Brexit vote, US Presidential and 2018 Midterm elections, Swedish elections, German elections, Hong Kong protests), we decided to apply a label if the account is propag”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “al data through smartphones, personal assistant devices, and social media that can model individuals better than other people. Recent negative interference of social media bots in political elections [91, 212] were yet another sign of how susceptible our lives are to the misuse of artificial intelli”