The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
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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Cites
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Research works printed but not linked: 4
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Bécue, A., Praça, I., & Gama, J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09942-2 · Cited by 368
False Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289
The Emerging Threat of Ai-driven Cyber Attacks: A Review
Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2037254 · Cited by 267
Unpacking the Social Media Bot: A Typology to Guide Research and Policy
Gorwa, R., & Guilbeault, D. (2020) · Policy & Internet · doi:10.1002/poi3.184 · Cited by 270
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194
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
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