Label propagation-based approach for detecting review spammer groups on e-commerce websites

Zhang, Fuzhi; Hao, Xinlong; Chao, Jinbo; Yuan, Shuai

doi:10.1016/j.knosys.2020.105520

Cited by 47 publications

(33 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cao et al [21] extracted the multiview anomalous features of collusive spammers and employed a hierarchical clustering algorithm to detect the spammer groups in location-based social networks. In our recent work [22], we used a Label propagation-based approach for detecting review spammer groups.…”

Section: Graph-based Detection Methodsmentioning

confidence: 99%

Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Chao

Zhao

Zhang

2022

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

Information security is one of the key issues in e-commerce Internet of Things (IoT) platform research. The collusive spamming groups on e-commerce platforms can write a large number of fake reviews over a period of time for the evaluated products, which seriously affect the purchase decision behaviors of consumers and destroy the fair competition environment among merchants. To address this problem, we propose a network embedding based approach to detect collusive spamming groups. First, we use the idea of a meta-graph to construct a heterogeneous information network based on the user review dataset. Second, we exploit the modified DeepWalk algorithm to learn the low-dimensional vector representations of user nodes in the heterogeneous information network and employ the clustering methods to obtain candidate spamming groups. Finally, we leverage an indicator weighting strategy to calculate the spamming score of each candidate group, and the top-k groups with high spamming scores are considered to be the collusive spamming groups. The experimental results on two real-world review datasets show that the overall detection performance of the proposed approach is much better than that of baseline methods.

show abstract

Section: Graph-based Detection Methodsmentioning

confidence: 99%

Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Chao

Zhao

Zhang

2022

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…They proposed a novel hierarchical approach in which they first derive distributions for key features that define reviewer behavior, and then combine these distributions into a finite mixture model. Zhang et al 7 proposed 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.…”

Section: Related Workmentioning

confidence: 99%

“…However, most extant studies on fake video detection focus on its content 4 , 5 . Although the detection of fake reviewer has been investigated by many prior studies 6 , 7 , the detection of fake video uploaders is still lacking. Detection of fake video uploaders is the key step to identify the bad intention of fake videos.…”

Section: Introductionmentioning

confidence: 99%

Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

Jia

et al. 2021

Sci Rep

View full text Add to dashboard Cite

With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.

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

Label propagation-based approach for detecting review spammer groups on e-commerce websites

Cited by 47 publications

References 17 publications

Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

Contact Info

Product

Resources

About