Why AI-assisted plagiarism makes fake review detection harder—and what can be done about it
Jul 21, 2025
Recent advances in generative AI, especially large language models (LLMs), have introduced new challenges in detecting fake online reviews. While AI-generated reviews from scratch are already hard to spot, paraphrased reviews—where real reviews are reworded using tools like ChatGPT—pose an even greater threat due to their human-like quality and harder-to-detect patterns.
Key takeaways
- Shift from manual to AI-driven fakes: Fake reviews no longer need to be written by paid users; LLMs can now automate the generation or paraphrasing of realistic reviews at scale.
- Two types of AI-generated reviews: Reviews can either be written from scratch by LLMs or paraphrased from existing authentic reviews—each posing different detection challenges.
- Paraphrasing mimics human tone: Because paraphrased reviews retain the style and sentiment of the original, they are harder to detect using traditional AI-generated content tools.
- Limitations of current detectors: Existing AI text detectors perform reasonably well with fully generated text but struggle with paraphrased content, making detection less reliable.
- New detection methodology proposed: The paper introduces a pattern-based detection method that outperforms many commercial and open-source tools, achieving over 90% across key accuracy metrics.
- Industry-wide concern: Platforms like Amazon, TripAdvisor, and Yelp are actively investing in review fraud prevention and transparency, while academic and tech communities explore detection solutions.
- Coalitions and policy responses: Cross-platform coalitions and government policies are emerging in response to the growing sophistication and scale of fake reviews.
- Broader implications of LLM misuse: While LLMs increase productivity, they also pose new ethical and reputational risks, requiring updated safeguards and detection technologies.
Get the full story at ScienceDirect
