Detecting Deceptive Reviews using Generative Adversarial Networks

by   Hojjat Aghakhani, et al.
The Regents of the University of California

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.


page 1

page 2

page 3

page 4


GANs for Semi-Supervised Opinion Spam Detection

Online reviews have become a vital source of information in purchasing a...

Bayesian GAN

Generative adversarial networks (GANs) can implicitly learn rich distrib...

Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

This paper investigates the potential of semi-supervised Generative Adve...

GANgster: A Fraud Review Detector based on Regulated GAN with Data Augmentation

Financial implications of written reviews provide great incentives for b...

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

As a new way of training generative models, Generative Adversarial Nets ...

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

Generative adversarial networks (GANs) are a recently proposed class of ...

Fuzzy Generative Adversarial Networks

Generative Adversarial Networks (GANs) are well-known tools for data gen...

Please sign up or login with your details

Forgot password? Click here to reset