Detecting Review Spammer Groups in Dynamic Review Networks

摘要：

　　Online product reviews are becoming the second most trust-ed source of product information,second only to recommen-dations from family and friends,because consumers think that online product reviews reflect recommendations of"real"people.However,in order to maximize the impact,some merchants organize a group of fraudulent reviewers to post a lot of fraudulent reviews that mislead consumers,which is called review spammer group.Solutions for review spammer group detection are very limited,and most solutions focus on static review networks.In this paper,we propose an on-line two-step framework,called OGSpam,detecting review spammer groups in dynamic review networks.By model a dynamic review network as an initial static review network with an infinite change review stream,our framework first detects reviewer groups on the initial static review network(first snapshot)based on classical Clique Percolation Method(CPM).Then,it detects reviewer groups on snapshot T+1 using reviewer network at T+1 and reviewer groups at T.The experimental results on two real-world review datasets illustrate the efficiency and effectiveness of our framework.To the best of our knowledge,this is the first time to detect review spammer group in dynamic review network.

关键词： review spam spammer group detection dynamic review net-work online learning clique percolation method

作者: Mengxiao Hu Guangxia Xu Chuang Ma Mahmoud Daneshmand

作者单位: School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing,China School of Software Engineering,Chongqing University of Posts and Telecommunications Information and School of Business Stevens Institute of Technology Hoboken,USA

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 383-388

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）