research-article

Uncovering collusive spammers in Chinese review websites

Authors:

Chang Xu,

Jie Zhang,

Kuiyu Chang,

Chong LongAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 979 - 988

https://doi.org/10.1145/2505515.2505700

Published: 27 October 2013 Publication History

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Abstract

As the rapid development of China's e-commerce in recent years and the underlying evolution of adversarial spamming tactics, more sophisticated spamming activities may carry out in Chinese review websites. Empirical analysis, on recently crawled product reviews from a popular Chinese e-commerce website, reveals the failure of many state-of-the-art spam indicators on detecting collusive spammers. Two novel methods are then proposed: 1) a KNN-based method that considers the pairwise similarity of two reviewers based on their group-level relational information and selects k most similar reviewers for voting; 2) a more general graph-based classification method that jointly classifies a set of reviewers based on their pairwise transaction correlations. Experimental results show that both our methods promisingly outperform the indicator-only classifiers in various settings.

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Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Iqbal AYounas MIftikhar SFatima FSaleem R(2025)Spam detection using hybrid model on fusion of spammer behavior and linguistics featuresEgyptian Informatics Journal10.1016/j.eij.2024.10060529(100605)Online publication date: Mar-2025
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Zhang YHao QZheng WXiao Y(2025)User similarity-based graph convolutional neural network for shilling attack detectionApplied Intelligence10.1007/s10489-025-06254-255:5Online publication date: 17-Jan-2025
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Index Terms

Uncovering collusive spammers in Chinese review websites
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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Association for Computing Machinery

New York, NY, United States

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Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Liu Y(2025)Signed Latent Factors for Spamming Activity DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351657320(651-664)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3516573
Iqbal AYounas MIftikhar SFatima FSaleem R(2025)Spam detection using hybrid model on fusion of spammer behavior and linguistics featuresEgyptian Informatics Journal10.1016/j.eij.2024.10060529(100605)Online publication date: Mar-2025
https://doi.org/10.1016/j.eij.2024.100605
Zhang YHao QZheng WXiao Y(2025)User similarity-based graph convolutional neural network for shilling attack detectionApplied Intelligence10.1007/s10489-025-06254-255:5Online publication date: 17-Jan-2025
https://doi.org/10.1007/s10489-025-06254-2
Wang CLi NJi SFang XWang Z(2024)Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimizationCybersecurity10.1186/s42400-024-00230-y7:1Online publication date: 4-Jun-2024
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Zhou QHuang C(2024)A recommendation attack detection approach integrating CNN with BaggingComputers and Security10.1016/j.cose.2024.104030146:COnline publication date: 1-Nov-2024
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Nian FWang CZhang DDang Z(2024)A study on the propagation of online public opinion by internet water armySocial Network Analysis and Mining10.1007/s13278-023-01182-w14:1Online publication date: 17-Jan-2024
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Duma RNiu ZNyamawe ATchaye-Kondi JJingili NYusuf ADeve A(2024)Fake review detection techniques, issues, and future research directions: a literature reviewKnowledge and Information Systems10.1007/s10115-024-02118-266:9(5071-5112)Online publication date: 17-May-2024
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Huo CCui JMa RLuo YZhang F(2024)Spammer Group Detection Approach Based on Deep Reinforcement LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5606-3_20(233-244)Online publication date: 5-Aug-2024
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Peng JWang YMeng Y(2023)Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel IndustryJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1801000618:1(105-129)Online publication date: 5-Jan-2023
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Zhang QLiang ZJi SXing BChiu D(2023)Detecting fake reviewers in heterogeneous networks of buyers and sellers: a collaborative training-based spammer group algorithmCybersecurity10.1186/s42400-023-00159-86:1Online publication date: 2-Oct-2023
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Spotting opinion spammers using behavioral footprints

Detecting product review spammers using rating behaviors

Detecting collusive spammers in online review communities

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