# Automated Crowdturfing Attacks and Defenses in Online Review Systems

arXiv: https://arxiv.org/abs/1708.08151 · HTML: https://mlsanity.com/paper/1708.08151/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2017.

## Built on by (most cited first, 14 of 14)

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- [Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media](https://mlsanity.com/paper/2302.07731/) (2023)
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- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Deepfake Detection of Frontal Face Videos](https://mlsanity.com/paper/2311.02733/) (2023)
- [Attention-based Bidirectional LSTM for Deceptive Opinion Spam Classification](https://mlsanity.com/paper/2112.14789/) (2021)
- [Characterizing Improper Input Validation Vulnerabilities of Mobile Crowdsourcing Services](https://mlsanity.com/paper/2110.08517/) (2021)
- [Social Fraud Detection Review: Methods, Challenges and Analysis](https://mlsanity.com/paper/2111.05645/) (2021)
- [A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models](https://mlsanity.com/paper/2609.30292/) (2026)

## Abstract (arXiv)

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. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers.