Spotting review spammer groups: A cosine pattern and network based method. (27th May 2018)

• Record Type: Journal Article
• Authors: Zhang, Lu; He, Gaofeng; Cao, Jie; Zhu, Haiting; Xu, Bingfeng
• Other Names: Ricci Laura guestEditor.; Iosup Alexandru guestEditor.; Prodan Radu guestEditor.; Dong Fang guestEditor.; Shen Jun guestEditor.; He Qiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 20(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-27
• Subjects: cosine pattern -- heterogeneous information network -- review spammer group detecting -- tight spammer group; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4686
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Nowadays, online product reviews strongly influence the purchase decision of consumers in e‐commerce platforms. Driven by the immense financial profits, review spammers deliberately post fake reviews to promote or demote their target products. Some spammers are even organized as groups to work together and try to take total control of the sentiment on their target products. To detect such spammer groups, most previous works exploit frequent itemset mining (FIM) to find spammer group candidates and then use unsupervised spamicity ranking methods to identify real spammer groups. However, these methods usually suffer from the problem of threshold setting, ie, high support value finding fewer groups while low support value leading to more coincidentally generated groups and computational inefficiency. Moreover, the unsupervised methods are not able to make good use of labeled instances which are actually obtainable in practice. In this paper, we propose CONSGD, a cosine pattern and heterogeneous information network–based spammer group detecting method. Specifically, the CONSGD uses cosine pattern mining (CPM) to discover tight spammer group candidates with a respective low support value, where the cosine threshold is utilized to avoid coincidentally generated groups. Moreover, CONSGD employs heterogeneous information network classification to identify the real spammer groups, which could utilize the labeled instances and do not rely to the assumption of independent instances. Experiments on real‐life dataset show that our proposed CONSGD is effective and outperforms the state‐of‐the‐art spammer group detection methods.