Coordinated Behavior on Social Media in 2019 UK General Election
Open accessAbstract
Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and fine-grained results for studying online information manipulation.
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Key signals for unsupervised bot detection include temporal activities, common retweets, and URLs shared in tweets.
“Key signals include temporal activities (Chavoshi et al., 2016;Keller et al., 2017), common retweets (Nizzoli et al., 2021), and URLs shared in tweets (Pacheco et al., 2021;Giglietto et al., 2020).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Nizzoli et al., 2021
In “Surfacing Coordination in 2019 UK GE,” the paper models users by the tweets they retweeted and uses similarities between those users to investigate coordination. This supports the common-retweets signal attributed to Nizzoli et al., but not its characterization as a signal for bot detection: “Use of automation” says coordination and automation are different concepts. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCoordination in account activity does not necessarily coincide with automation.
“While coordination in account activity does not necessarily coincide with automation [36,37], accounts created on similar dates and following users successively are more likely to be automated [3,48].”
From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [36]
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References 37 · Cited by 95
Research works printed but not linked: 16
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- Authors
- Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., & Tesconi, M.
- Affiliations
- University of Pisa (IT)
- Institute of Informatics and Telematics (IT)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-05-22
- Volume
- 15
- Pages
- 443-454
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W3068316593
- Cited by
- 95
- References
- 37
- Citations by year
- 2026: 8 · 2025: 21 · 2024: 27 · 2023: 9 · 2022: 17 · 2021: 11 · 2020: 2
- FWCI
- 43.32
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- coordinated behavior, social media, 2019 UK general election, disinformation, information manipulation, election debates
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