  The promising performance of Deep Neural Networks (DNNs) in text
classification, has attracted researchers to use them for fraud review
detection. However, the lack of trusted labeled data has limited the
performance of the current solutions in detecting fraud reviews. The Generative
Adversarial Network (GAN) as a semi-supervised method has demonstrated to be
effective for data augmentation purposes. The state-of-the-art solutions
utilize GANs to overcome the data scarcity problem. However, they fail to
incorporate the behavioral clues in fraud generation. Additionally,
state-of-the-art approaches overlook the possible bot-generated reviews in the
dataset. Finally, they also suffer from a common limitation in scalability and
stability of the GAN, slowing down the training procedure. In this work, we
propose ScoreGAN for fraud review detection that makes use of both review text
and review rating scores in the generation and detection process. Scores are
incorporated through Information Gain Maximization (IGM) into the loss function
for three reasons. One is to generate score-correlated reviews based on the
scores given to the generator. Second, the generated reviews are employed to
train the discriminator, so the discriminator can correctly label the possible
bot-generated reviews through joint representations learned from the
concatenation of GLobal Vector for Word representation (GLoVe) extracted from
the text and the score. Finally, it can be used to improve the stability and
scalability of the GAN. Results show that the proposed framework outperformed
the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7\%,
and 5\% on the Yelp and TripAdvisor datasets, respectively.
