虚假评论检测综述：从预训练语言模型到大语言模型
原标题：A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models
AI 摘要
该综述从信息融合视角系统回顾了2018年至2026年初的211项虚假评论检测研究，覆盖文本、情感、评分行为、时序元数据、用户-商品图、多模态内容、外部知识及LLM生成信号等证据来源。文章梳理了从传统机器学习、深度学习到PLM和LLM方法的演进，并分析了Amazon、Yelp、OpSpam等基准上的性能趋势及标签构建、数据划分和评估协议差异带来的局限。作者指出LLM既能生成流畅的欺骗性评论，也能提供更强的语义表示用于检测，并提出了对抗生成、跨域迁移、不确定性融合、可解释性和可信评估等开放问题。
正文节选
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 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, or LLMs, has changed the problem in two directions. LLMs can generate fluent and context-aware deceptive reviews, whil