Online reviews shape consumer decisions, platform governance, and corporate reputation.
Fake reviews compromise this information channel by injecting deceptive evidence into rating systems, recommendation pipelines, and public trust mechanisms.
The Rise of Large Language Models
The rise of large language models, or LLMs, has changed the problem in two directions.
- LLMs can generate fluent and context-aware deceptive reviews.
- Pre-trained language models, or PLMs, and LLMs also provide stronger semantic representations for detection.
This Survey
This survey reviews fake review detection from an information fusion perspective, covering 211 studies published from 2018 to early 2026.
We organize existing work by evidence source and fusion level, covering review text, sentiment, rating behavior, temporal metadata, user-product graphs, multimodal content, external knowledge, and LLM-generated signals.
Development of Detection Methods
We trace the development from traditional machine learning and deep learning to PLM-based and LLM-based methods, and examine how different approaches combine textual, behavioral, structural, and multimodal evidence.
Performance Trends and Limitations
We also analyze reported performance trends on widely used Amazon, Yelp, and OpSpam benchmark families, while noting the limitations caused by different label construction procedures, data splits, and evaluation protocols.
Open Problems
Finally, we identify open problems in adversarial generation, cross-domain transfer, uncertainty-aware fusion, missing-source robustness, interpretability, and trustworthy evaluation for AI-generated deceptive content.