Detecting Clusters of Fake Accounts in Online Social Networks
Abstract
Fake accounts are a preferred means for malicious users of online social networks to send spam, commit fraud, or otherwise abuse the system. A single malicious actor may create dozens to thousands of fake accounts in order to scale their operation to reach the maximum number of legitimate members. Detecting and taking action on these accounts as quickly as possible is imperative in order to protect legitimate members and maintain the trustworthiness of the network. However, any individual fake account may appear to be legitimate on first inspection, for example by having a real-sounding name or a believable profile.
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Supervised machine learning models can detect fraudulent activities with high accuracy given a ground truth dataset and reasonable feature engineering.
“With a ground truth dataset and reasonable feature engineering, it is often possible to build high accuracy supervised machine learning models to detect such activities”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [98]
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- Authors
- Xiao, C., Freeman, D. M., & Hwa, T.
- Affiliations
- University of Washington (US)
- LinkedIn (United States) (US)
- Venue
- Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security
- Publisher
- ACM
- Published
- 2015-10-16
- Pages
- 91-101
- OpenAlex
- W1978054023
- Cited by
- 184
- References
- 36
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 13 · 2023: 20 · 2022: 14 · 2021: 17 · 2020: 26 · 2019: 25 · 2018: 25 · 2017: 19 · 2016: 7 · 2015: 2
- FWCI
- 15.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Artificial Intelligence and Security (AISec) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fake accounts, social networking sites, malicious user detection, spam, fraud, network trustworthiness
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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