# Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media

arXiv: https://arxiv.org/abs/2302.07731 · HTML: https://mlsanity.com/paper/2302.07731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 105 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Detecting Photoshopped Faces by Scripting Photoshop](https://mlsanity.com/paper/1906.05856/) (2019)
- [Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Test](https://mlsanity.com/paper/2107.03448/) (2021)
- [Automated Crowdturfing Attacks and Defenses in Online Review Systems](https://mlsanity.com/paper/1708.08151/) (2017)
- [A framework for fake review detection in online consumer electronics retailers](https://mlsanity.com/paper/1903.12452/) (2019)
- [The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level](https://mlsanity.com/paper/2203.04088/) (2022)
- [Detecting Singleton Review Spammers Using Semantic Similarity](https://mlsanity.com/paper/1609.02727/) (2016)
- [Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training](https://mlsanity.com/paper/2012.13400/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go](https://mlsanity.com/paper/2412.05445/) (2024)
- [Detector-Evasive LLM Paraphrasing via Constrained Policy Optimization](https://mlsanity.com/paper/2606.00392/) (2026)
- [BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets](https://mlsanity.com/paper/2405.11192/) (2024)
- [The Role of AI in Online Reviews](https://mlsanity.com/paper/2609.22198/) (2026)

## Abstract (arXiv)

Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, thus posing challenges for social media platforms to detect these machine-generated fake reviews. We propose to leverage the high-quality elite restaurant reviews verified by Yelp to generate fake reviews from the OpenAI GPT review creator and ultimately fine-tune a GPT output detector to predict fake reviews that significantly outperform existing solutions. We further apply the model to predict non-elite reviews and identify the patterns across several dimensions, such as review, user and restaurant characteristics, and writing style. We show that social media platforms are continuously challenged by machine-generated fake reviews, although they may implement detection systems to filter out suspicious reviews.