MAGAT-HOS: A Multi-Attention Graph Neural Network for Fake Review Detection by Incorporating High-Order Semantic Information

The proliferation of fake reviews on e-commerce platforms has seriously and negatively affected consumers’ purchase decisions. In recent years, some researchers have started applying graph neural networks for fake review detection and achieved better performance than other machine learning and deep learning models. However, the existing work has rarely considered the influence of edge feature information on nodes when updating node information and only used the contextual order of words in the text when constructing a graph. In this paper, we propose a new method for constructing graphs and present a multi-attention graph neural network (MAGAT-HOS) that considers both node-level attention and edge-level attention. When creating the edges of the graph, we not only consider word context order semantic information, but also high-order semantic information such as the named entity information within review text. In addition, the graph learns node representations with two different aggregation functions, which are implemented based on node-level attention and edge-level attention. At last, all the nodes’ features are aggregated using a self-attention mechanism to emphasize the important information. Evaluation results show that the model and graph structure construction method proposed in this paper achieves better performance than the state-of-the-art baseline models.