# Detecting Deceptive Reviews using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1805.10364 · HTML: https://mlsanity.com/paper/1805.10364/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 12 of 12)

- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature](https://mlsanity.com/paper/2106.05261/) (2021)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [Deception detection in text and its relation to the cultural dimension of individualism/collectivism](https://mlsanity.com/paper/2105.12530/) (2021)
- [Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks](https://mlsanity.com/paper/2304.02739/) (2023)
- [ApproxED: Approximate exploitability descent via learned best responses](https://mlsanity.com/paper/2301.08830/) (2023)
- [Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains](https://mlsanity.com/paper/2504.06917/) (2025)
- [Social Fraud Detection Review: Methods, Challenges and Analysis](https://mlsanity.com/paper/2111.05645/) (2021)
- [A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)](https://mlsanity.com/paper/2208.11532/) (2022)
- [Adversarial Botometer: Adversarial Analysis for Social Bot Detection](https://mlsanity.com/paper/2405.02016/) (2024)
- [Truth in Text: A Meta-Analysis of ML-Based Cyber Information Influence Detection Approaches](https://mlsanity.com/paper/2503.22686/) (2025)
- [A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models](https://mlsanity.com/paper/2609.30292/) (2026)

## Abstract (arXiv)

In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detecting deceptive reviews. Unlike standard GAN models which have a single Generator and Discriminator model, FakeGAN uses two discriminator models and one generative model. The generator is modeled as a stochastic policy agent in reinforcement learning (RL), and the discriminators use Monte Carlo search algorithm to estimate and pass the intermediate action-value as the RL reward to the generator. Providing the generator model with two discriminator models avoids the mod collapse issue by learning from both distributions of truthful and deceptive reviews. Indeed, our experiments show that using two discriminators provides FakeGAN high stability, which is a known issue for GAN architectures. While FakeGAN is built upon a semi-supervised classifier, known for less accuracy, our evaluation results on a dataset of TripAdvisor hotel reviews show the same performance in terms of accuracy as of the state-of-the-art approaches that apply supervised machine learning. These results indicate that GANs can be effective for text classification tasks. Specifically, FakeGAN is effective at detecting deceptive reviews.