Cited by 150Spam and Phishing DetectionMisinformation and Its ImpactsHate Speech and Cyberbullying Detection

Automated Crowdturfing Attacks and Defenses in Online Review Systems

Open access
Yao, Yuanshun, Viswanath, Bimal, Cryan, Jenna, Zheng, Haitao, Zhao, Ben Y.·2017·Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Abstract

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.

Integrity signals

Registry

Crossref · 10.1145/3133956.3133990

Cites

68 of 68 references verified0 citation errors · 0 not checkable

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  • Fraudsters synchronize the activities of accounts they control, producing lockstep behavior.

    “For the inter-review features, the statistics computed over L i j leverage the observation that fraudsters synchronize the activities of the accounts that they control, e.g., in a”

    From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [71]

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Authors
Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y.
Affiliations
  • University of Chicago (US)
  • University of Chicago, Chicago, IL, USA
Venue
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
Publisher
ACM
Published
2017-10-30
Pages
1143-1158
ISSN
1543-7221 (ISSN-L)
OpenAlex
W2752337926
Cited by
150
References
69
Citations by year
2025: 10 · 2024: 6 · 2023: 20 · 2022: 21 · 2021: 27 · 2020: 19 · 2019: 30 · 2018: 17
FWCI
13.86
Type
conference paper
Language
en
Open access
Locations
  • ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
NSF (CNS-1527939, CNS-1705042)
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
fake reviews, recurrent neural networks, misinformation, online review systems, attack detection, burstiness

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