# Survey reviews 211 fake review detection studies from 2018 to 2026

Digest AI · Research · published 2026-09-28T04:00:00Z

Canonical: https://digestai.news/story/survey-reviews-211-fake-review-detection-studies-from-2018-to-2026

## Summary

A new survey published on arXiv examines the evolution of fake review detection methods, covering 211 studies released between 2018 and early 2026. The paper analyzes how the field has shifted from traditional machine learning to pre-trained language models (PLMs) and large language models (LLMs). It highlights a dual challenge: LLMs can now generate highly convincing deceptive reviews, yet they also offer stronger semantic tools for identifying them.

The authors organize the literature by evidence source and fusion level, including review text, sentiment, rating behavior, temporal metadata, user-product graphs, and multimodal content. They trace the integration of textual, behavioral, structural, and external knowledge signals. The survey also evaluates performance trends on standard benchmarks such as Amazon, Yelp, and OpSpam, while noting that inconsistent label construction and evaluation protocols limit direct comparability across studies.

The paper concludes by identifying open problems in the field, including adversarial generation, cross-domain transfer, and the need for uncertainty-aware fusion. It emphasizes the growing difficulty of detecting AI-generated deceptive content and calls for more trustworthy evaluation methods to maintain platform integrity.

## Key points

- Survey covers 211 studies on fake review detection published from 2018 to early 2026.
- Analyzes shift from traditional ML to PLM and LLM-based detection methods.
- Identifies open problems including adversarial generation and cross-domain transfer.

## Why it matters

As LLMs make fake reviews more convincing, understanding detection methods is critical for platform trust. This survey provides a roadmap for researchers and developers to improve robustness against AI-generated deception.

## Sources

1. [A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models](https://arxiv.org/abs/2609.30292) (arXiv cs.CL, 2026-09-28, primary source)

## Cite

Digest AI, "Survey reviews 211 fake review detection studies from 2018 to 2026", 28 September 2026, https://digestai.news/story/survey-reviews-211-fake-review-detection-studies-from-2018-to-2026

---

Written by Digest AI's editorial model from the linked sources; the sources are the record. Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse
JSON: https://digestai.news/story/survey-reviews-211-fake-review-detection-studies-from-2018-to-2026.json