# GANs for Semi-Supervised Opinion Spam Detection

## TL;DR

This paper introduces spamGAN, a generative adversarial network that effectively detects opinion spam using limited labeled data and unlabeled reviews, outperforming existing methods and generating realistic reviews.

## Contribution

The paper presents spamGAN, a novel GAN-based approach that enhances opinion spam detection with minimal labeled data and also generates plausible reviews.

## Key findings

- spamGAN outperforms existing spam detection methods with limited labeled data
- spamGAN can generate reviews with reasonable perplexity
- The approach improves text classification in opinion spam detection

## Abstract

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them have been labeled spam or non-spam. In this paper, we propose spamGAN, a generative adversarial network which relies on limited set of labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor dataset show that spamGAN outperforms existing spam detection techniques when limited labeled data is used. Apart from detecting spam reviews, spamGAN can also generate reviews with reasonable perplexity.