Fake or Genuine? Contextualised Text Representation for Fake Review Detection
This addresses the issue of fake reviews misleading consumers and companies, though it appears incremental as it builds on existing transformer methods.
The paper tackled the problem of low accuracy in fake review detection by proposing a new ensemble model using transformer architecture to capture semantic meaning, achieving superior performance over state-of-the-art models on semi-real benchmark datasets.
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.