Label propagationbased approach for detecting review spammer groups on ecommerce websites Abstract   Online product reviews are very important information resources on ecommerce websites and significantly influence consumersxe2x80x99 purchase decisions. Driven by interests, however, some merchants might hirexc2xa0a 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 ecommerce websites than individual review spammers. To address this issue, in this paper we propose a label propagationbased approach to detect review spammer groups on ecommerce 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 topk review spammer groups. The experimental results of the realworld Amazon and Yelp datasets show that the proposed approach performs better than the baseline methods.

Learning Review Representations from user and Product Level Information for Spam Detection Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex global semantics of reviews. Although recent works apply deep learning methods to learn reviewlevel semantic features, their models ignore the impact of the userlevel and productlevel information on learning review semantics and the inherent userreviewproduct relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from user and product level. Specifically, we design a multiattention unit to extract user(product)related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10% absolute precision improvement over the stateoftheart performances on four realworld datasets, which show the effectiveness and versatility of the model.