AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 12 Sep 2026] Title:A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models View a PDF of the paper titled A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models, by Fanji Yang (1) and 7 other authors View PDF HTML (experimental) 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. Comments: Fanji Yang and Huiyao Chen contributed equally to this work. Accepted for publication in Information Fusion Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.30292 [cs.CL] (or arXiv:2609.30292v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.30292 arXiv-issued DOI via DataCite Related DOI: https://doi.org/10.1016/j.inffus.2026.104715 DOI(s) linking to related resources Submission history From: Fanji Yang [view email] [v1] Sat, 12 Sep 2026 08:22:14 UTC (1,288 KB) Full-text links: Access Paper: View a PDF of the paper titled A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models, by Fanji Yang (1) and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)