research-article

Bot Electioneering Volume: Visualizing Social Bot Activity During Elections

Authors:

Kai-Cheng Yang,

Pik-Mai Hui,

Filippo MenczerAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 214 - 217

https://doi.org/10.1145/3308560.3316499

Published: 13 May 2019 Publication History

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Abstract

It has been widely recognized that automated bots may have a significant impact on the outcomes of national events. It is important to raise public awareness about the threat of bots on social media during these important events, such as the 2018 US midterm election. To this end, we deployed a web application to help the public explore the activities of likely bots on Twitter on a daily basis. The application, called Bot Electioneering Volume (BEV), reports on the level of likely bot activities and visualizes the topics targeted by them. With this paper we release our code base for the BEV framework, with the goal of facilitating future efforts to combat malicious bots on social media.

References

[1]

Alessandro Bessi and Emilio Ferrara. 2016. Social bots distort the 2016 US Presidential election online discussion. First Monday21, 11 (2016).

Google Scholar

[2]

Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Athena Vakali. 2017. Measuring #gamergate: A tale of hate, sexism, and bullying. In Proceedings of the 26th international conference on world wide web companion. International World Wide Web Conferences Steering Committee, IW3C2, Perth, 1285–1290.

Digital Library

Google Scholar

[3]

Michael D Conover, Bruno Gonçalves, Alessandro Flammini, and Filippo Menczer. 2012. Partisan asymmetries in online political activity. EPJ Data Science1(2012), 6.

Google Scholar

[4]

Clayton Allen Davis, Onur Varol, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2016. BotOrNot: A system to evaluate social bots. In Proc. 25th Intl. Conf. Companion on World Wide Web. IW3CSC, Montreal, 273–274.

Digital Library

Google Scholar

[5]

Emilio Ferrara. 2017. Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election. First Monday22, 8 (2017).

Google Scholar

[6]

Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM59, 7 (2016), 96–104.

Digital Library

Google Scholar

[7]

Panagiotis T Metaxas and Eni Mustafaraj. 2012. Social media and the elections. Science338, 6106 (2012), 472–473.

Google Scholar

[8]

Jacob Ratkiewicz, Michael Conover, Mark R Meiss, Bruno Gonçalves, Alessandro Flammini, and Filippo Menczer. 2011. Detecting and tracking political abuse in social media. In Proc. Intl. Conf. on Weblogs and Social Media (ICWSM). AAAI, Barcelona, 297–304.

Google Scholar

[9]

Chengcheng Shao, Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. 2016. Hoaxy: A platform for tracking online misinformation. In Proc. 25th Intl. Conf. Companion on World Wide Web. IW3CSC, Montreal, 745–750.

Digital Library

Google Scholar

[10]

Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. 2018. The spread of low-credibility content by social bots. Nat. Commun.9(2018), 4787.

Crossref

Google Scholar

[11]

Chengcheng Shao, Pik-Mai Hui, Lei Wang, Xinwen Jiang, Alessandro Flammini, Filippo Menczer, and Giovanni Luca Ciampaglia. 2018. Anatomy of an online misinformation network. PLoS ONE13, 4 (2018), e0196087.

Google Scholar

[12]

Massimo Stella, Emilio Ferrara, and Manlio De Domenico. 2018. Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. U.S.A.115, 49 (2018), 12435–12440.

Crossref

Google Scholar

[13]

Onur Varol, Emilio Ferrara, Clayton A Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. In Proc. Intl. Conf. on Web and Social Media (ICWSM). AAAI, Montreal, 280–289.

Google Scholar

[14]

Kai-Cheng Yang, Onur Varol, Clayton A Davis, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2019. Arming the public with AI to counter social bots. Human Behavior and Emerging Technologies1, 1 (2019).

Google Scholar

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Wei ZXu XHui PChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global ElectionsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652003(1126-1129)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3652003
Pacheco DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Bots, Elections, and Controversies: Twitter Insights from Brazil's Polarised ElectionsProceedings of the ACM Web Conference 202410.1145/3589334.3645651(2651-2659)Online publication date: 13-May-2024
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Kolomeets MChechulin A(2023)Properties of Malicious Social BotsProceedings of Telecommunication Universities10.31854/1813-324X-2023-9-1-94-1049:1(94-104)Online publication date: 13-Mar-2023
https://doi.org/10.31854/1813-324X-2023-9-1-94-104
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Index Terms

Bot Electioneering Volume: Visualizing Social Bot Activity During Elections

Index terms have been assigned to the content through auto-classification.

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WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Wei ZXu XHui PChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global ElectionsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652003(1126-1129)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3652003
Pacheco DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Bots, Elections, and Controversies: Twitter Insights from Brazil's Polarised ElectionsProceedings of the ACM Web Conference 202410.1145/3589334.3645651(2651-2659)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645651
Kolomeets MChechulin A(2023)Properties of Malicious Social BotsProceedings of Telecommunication Universities10.31854/1813-324X-2023-9-1-94-1049:1(94-104)Online publication date: 13-Mar-2023
https://doi.org/10.31854/1813-324X-2023-9-1-94-104
Zhou MZhang DWang YGeng YTang JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Detecting Social Bot on the Fly using Contrastive LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615468(4995-5001)Online publication date: 21-Oct-2023
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Ginossar TCruickshank IZheleva ESulskis JBerger-Wolf T(2022)Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversationsHuman Vaccines & Immunotherapeutics10.1080/21645515.2021.200364718:1(1-13)Online publication date: 21-Jan-2022
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