DeBot: Twitter Bot Detection via Warped Correlation
Abstract
We develop a warped correlation finder to identify correlated user accounts in social media websites such as Twitter. The key observation is that humans cannot be highly synchronous for a long duration, thus, highly synchronous user accounts are most likely bots. Existing bot detection methods are mostly supervised, which requires a large amount of labeled data to train, and do not consider cross-user features. In contrast, our bot detection system works on activity correlation without requiring labeled data. We develop a novel lag-sensitive hashing technique to cluster user accounts into correlated sets in near real-time. Our method, named DeBot, detects thousands of bots per day with a 94% precision and generates reports online everyday. In September 2016, DeBot has accumulated about 544,868 unique bots in the previous one year. We compare our detection technique with per-user techniques and with Twitter's suspension system. We observe that some bots can avoid Twitter's suspension mechanism and remain active for months, and, more alarmingly, we show that DeBot detects bots at a rate higher than the rate Twitter is suspending them.
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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 Chavoshi et al., 2016
Facebook, Twitter and Reddit banned tens of thousands of accounts involved in coordinated activities since 2016.
“These results are backed by the same platforms where information operations took place - namely, Facebook 2 , Twitter 3 and Reddit 4 - that banned tens of thousands accounts involved in coordinated activities since 2016.”
From A decade of social bot detection (2020) · cites this paper as [2]
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Aydın, İ., Sevi, M., & Salur, M. U. (2018) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2018.8620830 · Cited by 20 · see citing works
Unsupervised detection of coordinated fake-follower campaigns on social media
Zouzou, Y., & Varol, O. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00499-6 · Cited by 19 · see citing works
Bot and Gender Detection of Twitter Accounts Using Distortion and LSA.
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MulBot: Unsupervised Bot Detection Based on Multivariate Time Series
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Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning Algorithms
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LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot ClassifiersPreprint
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Something draws near, I can feel it: An analysis of human and bot emotion-exchange motifs on Twitter
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- Authors
- Chavoshi, N., Hamooni, H., & Mueen, A.
- Affiliations
- University of New Mexico (US)
- ORCID
- Mueen, Abdullah
- Venue
- 2016 IEEE 16th International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 817-822
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2583516892
- Cited by
- 260
- References
- 17
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 25 · 2023: 34 · 2022: 31 · 2021: 27 · 2020: 59 · 2019: 39 · 2018: 18 · 2017: 9
- FWCI
- 18.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- Twitter bot detection, social bots, activity synchronization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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