Coordination patterns reveal online political astroturfing across the world
Open accessAbstract
Online political astroturfing—hidden information campaigns in which a political actor mimics genuine citizen behavior by incentivizing agents to spread information online—has become prevalent on social media. Such inauthentic information campaigns threaten to undermine the Internet’s promise to more equitable participation in public debates. We argue that the logic of social behavior within the campaign bureaucracy and principal–agent problems lead to detectable activity patterns among the campaign’s social media accounts. Our analysis uses a network-based methodology to identify such coordination patterns in all campaigns contained in the largest publicly available database on astroturfing published by Twitter. On average, 74% of the involved accounts in each campaign engaged in a simple form of coordination that we call co-tweeting and co-retweeting. Comparing the astroturfing accounts to various systematically constructed comparison samples, we show that the same behavior is negligible among the accounts of regular users that the campaigns try to mimic. As its main substantive contribution, the paper demonstrates that online political astroturfing consistently leaves similar traces of coordination, even across diverse political and country contexts and different time periods. The presented methodology is a reliable first step for detecting astroturfing campaigns.
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Without explicit countermeasures, these systems will face the same coordinated inauthentic behavior that has plagued human social media.
“Our results suggest that without explicit countermeasures, these systems will face the same coordinated inauthentic behavior that has plagued human social media 17,23,24 .”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [24]
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References 31 · Cited by 64
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- [1]Bail CA Guay B Maloney E Combs A Hillygus DS Merhout F Freelon D Volfovsky A Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late (2017) Proc. Natl. Acad. Sci. 2020 117 1 243 250 10.1073/pnas.1906420116 31767743 PMC6955293 · DOI · Paper page
- [4]Allen Jennifer Howland Baird Mobius Markus Rothschild David Watts Duncan J Evaluating the fake news problem at the scale of the information ecosystem Sci. Adv. 2020 6 14 eaay3539 10.1126/sciadv.aay3539 32284969 PMC7124954 · DOI · Paper page
- [5]Guess Andrew M Nyhan Brendan Reifler Jason Exposure to untrustworthy websites in the 2016 US election Nat. Hum. Behav. 2020 4 472 480 10.1038/s41562-020-0833-x 32123342 PMC7239673 · DOI · Paper page
- [7]Ferrara Emilio Varol Onur Davis Clayton Menczer Filippo Flammini Alessandro The rise of social bots Commun. ACM 2016 59 7 96 104 10.1145/2818717 · DOI · Paper page
- [8]Stella Massimo Ferrara Emilio De Domenico Manlio Bots increase exposure to negative and inflammatory content in online social systems Proc. Natl. Acad. Sci. 2018 115 49 12435 12440 10.1073/pnas.1803470115 30459270 PMC6298098 · DOI · Paper page
- [9]Stukal Denis Sanovich Sergey Bonneau Richard Tucker Joshua A Detecting bots on Russian political Twitter Big Data 2017 5 4 310 324 10.1089/big.2017.0038 29235918 · DOI
- [12]Kovic, M., Rauchfleisch, A., Sele, M. & Caspar, C. Digital astroturfing in politics: Definition, typology, and countermeasures. Stud. Commun. Sci. 18 (1)10.24434/j.scoms.2020.02.005 (2018). · DOI
- [13]Lazer David MJ Baum Matthew A Benkler Yochai Berinsky Adam J Greenhill Kelly M Menczer Filippo Metzger Miriam J Nyhan Brendan Pennycook Gordon Rothschild David Schudson Michael Sloman Steven A Sunstein Cass R Thorson Emily A Watts Duncan J Zittrain Jonathan L The science of fake news Science 2018 359 6380 1094 1096 10.1126/science.aao2998 29590025 · DOI · Paper page
- [15]Freelon Deen Wells Chris Disinformation as political communication Polit. Commun. 2020 37 2 145 156 10.1080/10584609.2020.1723755 · DOI
- [16]Krafft PM Donovan Joan Disinformation by design: The use of evidence collages and platform filtering in a media manipulation campaign Polit. Commun. 2020 37 2 194 214 10.1080/10584609.2019.1686094 · DOI
- [19]Keller, F., Schoch, D., Stier, S. & Yang, J.. How to manipulate social media: Analyzing political astroturfing using ground truth data from South Korea. In Proceedings of the Eleventh International AAAI Conference on Web and Social Media, pp. 564–567, (The AAAI Press, 2017).
- [20]Kargar Simin Rauchfleisch Adrian State-aligned trolling in Iran and the double-edged affordances of Instagram New Med. Soc. 2019 21 7 1506 1527 10.1177/1461444818825133 · DOI
- [22]Miller Gary J The political evolution of principal–agent models Annu. Rev. Polit. Sci. 2005 8 203 225 10.1146/annurev.polisci.8.082103.104840 · DOI
- [23]Enos Ryan D Hersh Eitan D Party activists as campaign advertisers: The ground campaign as a principal–agent problem Am. Polit. Sci. Rev. 2015 109 2 252 278 10.1017/S0003055415000064 · DOI
- [27]Badawy, A., Ferrara, E. & Lerman, K. Analyzing the digital traces of political manipulation: The 2016 Russian interference Twitter campaign. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 258–265, (2018). 10.1109/ASONAM.2018.8508646. · DOI
- [29]Linvill Darren L Warren Patrick L Troll factories: Manufacturing specialized disinformation on Twitter Polit. Commun. 2020 37 4 447 467 10.1080/10584609.2020.1718257 · DOI
- [30]Lukito Josephine Suk Jiyoun Zhang Yini Doroshenko Larissa Kim Sang Jung Su Min-Hsin Xia Yiping Freelon Deen Wells Chris The wolves in sheep’s clothing: How Russia’s internet research agency tweets appeared in U.S. news as vox populi Int. J. Press/Polit. 2020 25 2 196 216 10.1177/1940161219895215 · DOI
- [31]Alizadeh M Shapiro JN Buntain C Tucker JA Content-based features predict social media influence operations Sci. Adv. 2020 10.1126/sciadv.abb5824 32832674 PMC7439640 · DOI · Paper page
- [32]Guarino S Trino N Celestini A Chessa A Riotta G Characterizing networks of propaganda on Twitter: A case study Appl. Netw. Sci. 2020 10.1007/s41109-020-00286-y · DOI · Paper page
- [33]Gurajala, S., White, J. S., Hudson, B. & Matthews, J. N. Fake Twitter accounts: Profile characteristics obtained using an activity-based pattern detection approach. In Proceedings of the 2015 International Conference on Social Media & Society - SMSociety ’15, pp. 1–7, (ACM Press, 2015). 10.1145/2789187.2789206. · DOI
- [34]Vargas, L., Emami, P. & Traynor, P. On the detection of disinformation campaign activity with network analysis, (2020). URL https://arxiv.org/abs/2005.13466. · arXiv
- Authors
- Schoch, D., Keller, F. B., Stier, S., & Yang, J.
- Affiliations
- GESIS - Leibniz Institute for the Social Sciences (DE)
- University of Bern (CH)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Schoch, David
- Venue
- Scientific Reports
- Published
- 2022-03-17
- Volume
- 12
- Issue
- 1
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8930979
- OpenAlex
- W4229000065
- Cited by
- 64
- References
- 31
- Citations by year
- 2026: 19 · 2025: 17 · 2024: 15 · 2023: 9 · 2022: 4
- FWCI
- 16.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (publishedVersion)
- MADOC (University of Mannheim) (acceptedVersion, cc-by)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) (submittedVersion, cc-by)
- Funders
- GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- online political campaigns, social media accounts, coordination patterns, principal-agent problem, Twitter, network analysis
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