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Automated Crowdturfing Attacks and Defenses in Online Review Systems
Open accessAbstract
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.
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“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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Mahbub, S., Pardede, E., Kayes, A. S. M., & Rahayu, W. (2019) · International Journal of Web and Grid Services · doi:10.1504/ijwgs.2019.099561 · Cited by 29
Metadata
- 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
- Semantic Scholar
- 352b2cfd65b5a1231e5e64bb1b9d6ed71e5a60a2
- 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
- green
- arxiv.org
- 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
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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