A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models

Abstract:Online reviews shape consumer decisions, platform governance, and corporate this http URL reviews compromise this information channel by injecting deceptive evidence into rating systems, recommendation pipelines, and public trust this http URL rise of large language models, or LLMs, has changed the problem in two this http URL can generate fluent and context-aware deceptive reviews, while pre-trained language models, or PLMs, and LLMs also provide stronger semantic representations for this http URL survey reviews fake review detection from an information fusion perspective, covering 211 studies published from 2018 to early this http URL 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 this http URL 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 this http URL 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 this http URL, 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.

Fanji Yang and Huiyao Chen contributed equally to this work. Accepted for publication in Information Fusion

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.30292 [cs.CL]

(or arXiv:2609.30292v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30292 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.inffus.2026.104715 Focus to learn more DOI(s) linking to related resources

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