Fake or Genuine? Contextualised Text Representation for Fake Review Detection

TL;DR

This paper introduces an ensemble transformer-based model that captures semantic nuances in reviews to improve the accuracy of fake review detection, outperforming existing models.

Contribution

It proposes a novel ensemble transformer architecture that leverages semantic understanding for more accurate fake review detection.

Findings

Outperforms state-of-the-art models on benchmark datasets

Effectively captures semantic features of reviews

Enhances robustness in fake review detection

Abstract

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.