  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.
