Spotting review spammer groups: A cosine pattern and network based method

Abstract

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. KEYWORDScosine pattern, heterogeneous information network, review spammer group detecting, tight spammer group INTRODUCTIONNowadays, the consumers often read product reviews before they buy the product in e-commerce platforms. When the reviews of one product are positive, the consumer will be very likely to buy it. On the contrary, if most of the reviews are negative, the consumer will discard to buy the product and choose another one. Driven by the immense financial profits, some review spammers (also called opinion spammer) post fake reviews to promote their products or to demote their competitors' products. As the manipulated reviews are misleading more and more consumers in their decision making of purchase, detecting such fake reviews or their posters, ie, spammers, becomes a pressing issue.Ever since the problem of review spam detecting was proposed by Jindal and Liu, 1 many efforts have been done for fake review or individual review spammer detecting. 2-6 Recently, however, many spammers are organized to post fake reviews together to promote or demote some target products. These collusive spammers, also called spammer group, can be highly damaging as they have many accounts to review one product and therefore could take total control of the sentiment on this product. Meanwhile, the spammer group has more manpower to imperceptibly manipulate reviews so that each group member may no longer appear ...

Spammer group detection and diversification of customers’ reviews

“…They used a different machine learning algorithm to classify suspicious spammer groups. Zhang et al (2018) proposed a CONSGD method that used a cosine pattern and heterogeneous information network method to detect spammer groups. To find a tight spammer group candidate, they used the FP-Growth-like algorithm to find cosine patterns.…”

“…Considering the existing work on spam group detection, most of the related studies (Mukherjee, Liu & Glance, 2012;Allahbakhsh et al, 2013;Zhang, Wu & Cao, 2017;Zhou, Liu & Zhang, 2018) have used spammer behavioral features to detect spam groups. On the other hand, some researchers used graph-based techniques to identify suspicious spammer groups with a little focus on spammer behavioral features (Rayana & Akoglu, 2015;Li et al, 2017;Kaghazgaran, Caverlee & Squicciarini, 2018;Zhang et al, 2018;Xu & Zhang, 2016;Xu et al, 2019;Hu et al, 2019). This research aims to develop a framework that will use both behavioral and graph features.…”

Advances in cloud computing and big data analytics

“…Prior solutions 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. Zhang et al propose CONSGD, a cosine pattern and heterogeneous information network–based spammer group detecting method to make spammer detection more efficient . 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.…”