Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 23, December 2021

Fake or Genuine? Contextualised Text Representation for Fake Review Detection


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.