Label propagation-based approach for detecting review spammer groups on e-commerce websites

Online product reviews are very important information resources on e-commerce websites and significantly influence consumers' purchase decisions. Driven by interests, however, some merchants might hire a group of reviewers working together to promote or demote a set of target products by writing fake reviews. Such a collusive fraudulent reviewer group is generally termed a review spammer group and is more harmful to e-commerce websites than individual review spammers. To address this issue, in this paper we propose a label propagation-based approach to detect review spammer groups on e-commerce websites. First, based on the evaluation data of reviewers, we extract the associations between reviewers with respect to review time and product ratings to construct a relationship graph of reviewers. Second, we propose an improved label propagation algorithm with a propagation intensity and an automatic filtering mechanism to find candidate spammer groups based on the constructed reviewer relationship graph. Finally, we propose a ranking algorithm that combines the entropy method and the analytic hierarchy process to rank the candidate spammer groups and thus identify the top-k review spammer groups. The experimental results of the real-world Amazon and Yelp datasets show that the proposed approach performs better than the baseline methods. (c) 2020 Elsevier B.V. All rights reserved.