Detecting review fraud using metaheuristic graph neural networks

Abstract

AbstractAlthough online reviews play a critical role in shaping consumer behavior and establishing institutional trust, this reliance on online reviews has given rise to a proliferation of fake reviews. Consequently, there is an urgent need for the development of robust mechanisms to identify and mitigate these inauthentic reviews. In this study, we introduce an innovative approach for the detection of fake reviews, leveraging a metaheuristic graph neural network model. Our methodology begins by extracting distinctive features from review text, comprising of term frequency—inverse document frequency (TF-IDF) vectors and bidirectional encoder representation transformer (BERT) embedding vectors. We feed these features into a graph convolutional network (GCN) that uses message passing to label each review. Furthermore, we address the optimization of our graph neural network models using Harris Hawk and Cat Swarm optimization techniques. Our experiments reveal that optimization of the GNN with Harris Hawk Optimization yields a significantly enhanced model performance, further validating the efficacy of our detection framework. This research contributes to the ongoing efforts in the realm of online review authenticity and showcases the potential of advanced techniques to combat the proliferation of fraudulent reviews.