Investigating coordinated account creation using burst detection and network analysis
Open accessAbstract
Democracies around the world face the threat of manipulation of their electorates via coordinated online influence campaigns. Researchers have responded by developing valuable methods for finding automated accounts and identifying false information, but these valiant efforts often fall into a cat-and-mouse game with perpetrators who constantly change their behavior. This has forced several researchers to go beyond the detection of individual malicious actors by instead identifying the coordinated activity that propels potent information operations. In this vein, we provide rigorous quantitative evidence for the notion that sudden increases in Twitter account creations may provide early warnings of online information operations. Analysis of fourteen months of tweets discussing the 2020 U.S. elections revealed that accounts created during bursts exhibited more similar behavior, showed more agreement on mail-in voting and mask wearing, and were more likely to be bots and share links to low-credibility sites. In concert with other techniques for detecting nefarious activity, social media platforms could temporarily limit the influence of accounts created during these bursts. Given the advantages of combining multiple anti-misinformation methods, we join others in presenting a case for the need to develop more integrable methods for countering online influence campaigns.
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Accounts created on similar dates and following users successively are more likely to be automated.
“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 [3]
The Results section (‘Bot activity’; page unavailable) reports that account-creation burst days had a higher proportion of accounts classified as bots, supporting the similar-creation-dates part. The paper does not establish that accounts following users successively are more likely to be automated; its discussion of accounts following candidates in ‘Related work > Account creation bursts’ does not make that finding. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAccounts created in bursts in short periods exhibited similar behavior amongst themselves and were more likely to be bots.
“A recent study on coordinated online influence campaigns indicated that accounts created in bursts in short periods exhibited similar behavior amongst themselves and were more likely to be bots [3].”
From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [3]
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- Authors
- Bellutta, D., & Carley, K. M.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Bellutta, Daniele
- Venue
- Journal of Big Data
- Published
- 2023-02-10
- Volume
- 10
- Issue
- 1
- Pages
- 20-20
- ISSN
- 2196-1115 (ISSN-L)
- PMC
- PMC9913025
- OpenAlex
- W4319947748
- Cited by
- 15
- References
- 46
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 3
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal Of Big Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- John S. and James L. Knight Foundation
- Cognizant
- Office of Naval Research (N000141812106, N000141812108)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- burst detection, Twitter, information operations, election interference, 2020 US presidential election, bot detection, misinformation, mail-in voting, masking
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