GSCPM: CPM-Based Group Spamming Detection in Online Product Reviews

Xu, Guangxia; Hu, Mengxiao; Ma, Chuang; Daneshmand, Mahmoud

doi:10.1109/icc.2019.8761650

Cited by 18 publications

(22 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model can identify candidate spammer groups. Xu et al (2019) proposed a three-phase method called Group Spam Clique Percolation Method (GSCPM) which is based on the Clique Percolation Method (CPM). It is a graph-based method, which models review data as a reviewer graph then breaks this reviewer graph into k-clique clusters using CPM.…”

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

“…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.…”

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spammer group detection and diversification of customers’ reviews

Hussain

Mirza

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

show abstract

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

Section: Spammer Group Detection Methodsmentioning

confidence: 99%

Spammer group detection and diversification of customers’ reviews

Hussain

Mirza

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…[9]. Recent years have seen an increased research effort in detecting fraudster groups [10], [11], [12], [13]. It is widely accepted that individual fraudsters can cause significant damages to businesses, fraudster groups may be even more damaging because of their coordinated and considerate volume of fraud reviews that they can collectively produce.…”

Section: Introductionmentioning

confidence: 99%

“…by two categories: Frequent Itemset Mining (FIM) based [10], [13] approaches or graph-based [11], [12] approaches. An FIM-based algorithm generally follows a two-step process: first, candidate groups are determined based on the same set of items (itemset) reviewed by the reviewers.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

Shehnepoor,

Togneri,

Liu

et al. 2022

Preprint

View full text Add to dashboard Cite

Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups static networks, consequently overlooking the longitudinal behavior of a reviewer thus the dynamics of co-review relations among reviewers in a group. Hence, these approaches are incapable of excluding outlier reviewers, which are fraudsters intentionally camouflaging themselves in a group and genuine reviewers happen to co-review in fraudster groups. To address this issue, in this work, we propose to first capitalise on the effectiveness of the HIN-RNN in both reviewers' representation learning while capturing the collaboration between reviewers, we first utilize the HIN-RNN to model the co-review relations of reviewers in a group in a fixed time window of 28 days. We refer to this as spatial relation learning representation to signify the generalisability of this work to other networked scenarios. Then we use an RNN on the spatial relations to predict the spatiotemporal relations of reviewers in the group. In the third step, a Graph Convolution Network (GCN) refines the reviewers' vector representations using these predicted relations. These refined representations are then used to remove outlier reviewers. The average of the remaining reviewers' representation is then fed to a simple fully connected layer to predict if the group is a fraudster group or not. Exhaustive experiments of the proposed approach showed a 5% (4%), 12% (5%), 12% (5%) improvement over three of the most recent approaches on precision, recall, and F1-value over the Yelp (Amazon) dataset, respectively.

show abstract

“…Jia et al [4] employed the LDA model to extract language features and classified spam comments into false reviews and real comments. Xu et al [5] constructed suspicious commenter graphs based on user behavior characteristics, and detected spam posting groups based on CPM in a completely unsupervised manner. Li et al [6] proposed a new feature extraction method based on the characteristics of spammers and spam comments, and the gradient enhancement tree algorithm was used to construct the spam review classifier.…”

Section: Introductionmentioning

confidence: 99%

Spam Comment Recognition Based on Wide & Deep Learning

Fu¹,

Ergu²

2020

FSP

View full text Add to dashboard Cite

The flood of e-commerce platform spam comments affects consumers' purchasing decisions, which greatly damages the interests of consumers. In the process of spam comment recognition, the explicit discrete features of spam comments were usually used as the input of the model. This paper combines the implicit semantic features of spam comments and the explicit discrete features of spam comments to identify the spam comment. First, SMOTE oversampling method is used to balance positive and negative sample sets. Then, wide & deep model, a recommendation system model proposed by Google, is improved and applied to one of the public datasets of spam comment recognition and the commodity datasets collected from one of the biggest e-commerce platform in China. The experimental results show that the improved algorithm can achieve good results in both the gold-standard opinion spam datasets and the commodity datasets.

show abstract

GSCPM: CPM-Based Group Spamming Detection in Online Product Reviews

Cited by 18 publications

References 16 publications

Spammer group detection and diversification of customers’ reviews

Spammer group detection and diversification of customers’ reviews

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

Spam Comment Recognition Based on Wide & Deep Learning

Contact Info

Product

Resources

About