The article surveys fake review detection research, focusing on how pre‑trained language models (PLMs) and large language models (LLMs) influence both the generation of deceptive reviews and their detection. It reviews 211 studies from 2018 to early 2026, categorizing methods by evidence source—such as review text, sentiment, rating behavior, temporal metadata, user‑product graphs, multimodal content, external knowledge, and LLM‑generated signals—and by fusion level. The survey traces the evolution from traditional machine learning to PLM‑based and LLM‑based approaches, evaluates performance on Amazon, Yelp, and OpSpam benchmarks, and highlights open challenges including adversarial generation, cross‑domain transfer, uncertainty‑aware fusion, robustness to missing sources, interpretability, and trustworthy evaluation of AI‑generated deceptive content.
By Fanji Yang (Guizhou University of Finance and Economics), Huiyao Chen (Harbin Institute of Technology), Xi Yu (Guizhou University of Finance and Economics), Meishan Zhang (Harbin Institute of Technology), Xiaohong Xiao (Guizhou University of Commerce), Mingsen Deng (Guizhou University of Finance and Economics)
The study analyzes 17,012 app‑store reviews for six major generative‑AI apps, using BERTopic and RoBERTa to uncover topics and sentiment. Negative sentiment is most common around advertising, authentication, server reliability, and subscription pricing, with significant differences across apps—Claude shows the highest negative sentiment yet a highly enthusiastic user base. The authors also note geopolitical and privacy concerns for DeepSeek and propose a Trust Friction Score to quantify trust and usability barriers.
By Md Jafrin Hossain, Umme Nusrat Jahan, Shouvaggo Sharif Shammo
arXiv:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.
By Alexander M. Fichtl, Lukas Ellinger, Josefin Kelber, Kry\v{s}tof Ol\'ik, Georg Groh
AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities.
arXiv:2510.06105v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive ad...
By Batu El, James Zou
arXiv:2601. 11496v3 Announce Type: replace-cross Abstract: AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms.
By Eilam Shapira, Moshe Tennenholtz, Roi Reichart
arXiv:2605. 03202v2 Announce Type: replace Abstract: Large language models offer a tempting solution to address the peer review crisis.
By Joachim Baumann, Jiaxin Pei, Sanmi Koyejo, Dirk Hovy
arXiv:2511. 13480v2 Announce Type: replace-cross Abstract: This study focuses on understanding the complex dynamics between humans and AI systems by analyzing user reviews.
By Parisa Arbab, Xiaowen Fang
arXiv:2605.14542v2 Announce Type: replace
Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...
By Yuyan Chen
arXiv:2606. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.
By Lin Li, Qi Zhang, Xander Davies, Jianing Qiu, Yarin Gal
The paper introduces Vocalizer, a mobile app that lets users submit spoken online reviews enhanced by a large language model. A longitudinal study shows that users often use the AI to add detail and that interactive AI features boost confidence and willingness to share reviews. The authors also outline the benefits and challenges of embedding AI assistance in review-writing systems.
By Kavindu Perera, D\'aniel Szab\'o, Niels van Berkel, Aku Visuri, Chi-Lan Yang, Koji Yatani, Simo Hosio
The paper introduces Self‑Conditioning, an unsupervised, information‑theoretic estimator that measures the amount of external information in peer reviews. It compares the likelihood of a review under its original production context with the likelihood when that context is augmented by hints extracted from the review itself. On the IntelLabs benchmark, Self‑Conditioning can perfectly distinguish fully‑delegated reviews from machine‑polished ones, remains largely insensitive to surface rewriting, and shows that increased external input drives scores toward human‑like values, unlike standard ATD baselines.
By Matthieu Dubois, Pablo Piantanida, Fran\c{c}ois Yvon