Rami Mohawesh¹, Shuxiang Xu¹, Matthew Springer¹, Muna Al-Hawawreh² and Sumbal Maqsood¹, ¹University of Tasmania, Australia, ²University of New South Wales, Australian Defence Force Academy (ADFA), Australia

Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models.

Fake review, detection, Transformer, Ensemble, Deep learning.