mlsanityData to 14 Oct 2026

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

arXiv 2302.07731 ↗First posted 10 Feb 2023cs.CL cs.AI cs.IRcited by 5

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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.

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

14 of 105 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  5. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  6. 2017Decoupled Weight Decay Regularizationcited by 1,831
  7. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  8. 2019Detecting Photoshopped Faces by Scripting Photoshopcited by 55
  9. 2021Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Testcited by 17
  10. 2017Automated Crowdturfing Attacks and Defenses in Online Review Systemscited by 14
  11. 2019A framework for fake review detection in online consumer electronics retailerscited by 12
  12. 2022The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood levelcited by 4
2 more
  1. 2016Detecting Singleton Review Spammers Using Semantic Similaritycited by 3
  2. 2020Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Trainingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 2.

5 papers.

  1. 2024A Survey on Responsible Generative AI: What to Generate and What Notcited by 8
  2. 2024From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Gocited by 1
  3. 2026Detector-Evasive LLM Paraphrasing via Constrained Policy Optimizationcited by 1
  4. 2024BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweetscited by 0
  5. 2026The Role of AI in Online Reviewscited by 0