GLORIA: A Graph Convolutional Network-Based Approach for Review Spam Detection

Spam reviews contain untruthful content created with malevolent intent, to affect the overall reputation of a product, service or company. This content is commonly made by malicious users or automated programs (i.e., bots) that mimic human behaviour. With the recent boom of online review systems, performing accurate review spam detection has become of primary importance for a review platform, to mitigate the effect of malicious users responsible for untruthful content. In this work, we propose a review spam classification approach, named GLORIA, that adopts a graph representation of review data and trains a graph convolutional neural network for edge classification as a review spam detection model. In particular, GLORIA represents both users (i.e., authors of reviews) and products (i.e., reviewed items) as nodes of a heterogeneous graph, while it represents reviews as graph edges that connect each author of a review to the reviewed item. Features of users, products and reviews are associated with nodes and edges, respectively. Experiments performed on publicly available review datasets prove the effectiveness of the proposed approach compared with some state-of-the-art approaches.