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.

本文提出了一种名为 FakeGAN 的系统，它是在文本分类任务中首次采用生成对抗网络（GANs）进行增强，用于检测欺骗性评论。通过使用两个鉴别器模型和一个生成模型，FakeGAN 避免了模式崩溃的问题， 并在 TripAdvisor 酒店评论数据集上表现出与现有监督学习方法相当的性能。