Spammer group detection based on cascading and clustering of core figures
Abstract The problem of collaborative spamming in e-commerce is gradually increasing, and traditional spammer group detection algorithms usually seem cumbersome and time-consuming when dealing with massive user review data. Thus, this research proposes a spammer group detection algorithm based on ca...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Cybersecurity |
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| Online Access: | https://doi.org/10.1186/s42400-024-00313-w |
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| _version_ | 1849235098979794944 |
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| author | Qianqian Jiang Chunrong Zhang Ning Li Dickson K. W. Chiu Xianwen Fang Shujuan Ji |
| author_facet | Qianqian Jiang Chunrong Zhang Ning Li Dickson K. W. Chiu Xianwen Fang Shujuan Ji |
| author_sort | Qianqian Jiang |
| collection | DOAJ |
| description | Abstract The problem of collaborative spamming in e-commerce is gradually increasing, and traditional spammer group detection algorithms usually seem cumbersome and time-consuming when dealing with massive user review data. Thus, this research proposes a spammer group detection algorithm based on cascading and clustering of core figures. First, we extract user evaluation features to identify core figures. Then, we use four user interaction features to assess user collusion degree, construct a weighted homogeneous graph by cascading neighbor nodes around core figures, and apply the Louvain weighted clustering algorithm to obtain candidate groups. Finally, we classify candidate groups based on group spam features. Experimental results based on the Amazon reviews dataset demonstrate the algorithm's effectiveness in identifying groups of spammers. |
| format | Article |
| id | doaj-art-8c6b7b89c3fa4965ae3b3eb2be2b7dd4 |
| institution | Kabale University |
| issn | 2523-3246 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj-art-8c6b7b89c3fa4965ae3b3eb2be2b7dd42025-08-20T04:02:55ZengSpringerOpenCybersecurity2523-32462025-07-018112210.1186/s42400-024-00313-wSpammer group detection based on cascading and clustering of core figuresQianqian Jiang0Chunrong Zhang1Ning Li2Dickson K. W. Chiu3Xianwen Fang4Shujuan Ji5Shandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and TechnologyThe College of Earth Science and Engineering, Shandong University of Science and TechnologyShandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and TechnologyFaculty of Education, The University of Hong KongAnhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety, Anhui University of Science and TechnologyShandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and TechnologyAbstract The problem of collaborative spamming in e-commerce is gradually increasing, and traditional spammer group detection algorithms usually seem cumbersome and time-consuming when dealing with massive user review data. Thus, this research proposes a spammer group detection algorithm based on cascading and clustering of core figures. First, we extract user evaluation features to identify core figures. Then, we use four user interaction features to assess user collusion degree, construct a weighted homogeneous graph by cascading neighbor nodes around core figures, and apply the Louvain weighted clustering algorithm to obtain candidate groups. Finally, we classify candidate groups based on group spam features. Experimental results based on the Amazon reviews dataset demonstrate the algorithm's effectiveness in identifying groups of spammers.https://doi.org/10.1186/s42400-024-00313-wSpammer group detectionCascading and clustering of core figuresUser interaction featuresHeterogeneous graphWeighted homogeneous graph |
| spellingShingle | Qianqian Jiang Chunrong Zhang Ning Li Dickson K. W. Chiu Xianwen Fang Shujuan Ji Spammer group detection based on cascading and clustering of core figures Cybersecurity Spammer group detection Cascading and clustering of core figures User interaction features Heterogeneous graph Weighted homogeneous graph |
| title | Spammer group detection based on cascading and clustering of core figures |
| title_full | Spammer group detection based on cascading and clustering of core figures |
| title_fullStr | Spammer group detection based on cascading and clustering of core figures |
| title_full_unstemmed | Spammer group detection based on cascading and clustering of core figures |
| title_short | Spammer group detection based on cascading and clustering of core figures |
| title_sort | spammer group detection based on cascading and clustering of core figures |
| topic | Spammer group detection Cascading and clustering of core figures User interaction features Heterogeneous graph Weighted homogeneous graph |
| url | https://doi.org/10.1186/s42400-024-00313-w |
| work_keys_str_mv | AT qianqianjiang spammergroupdetectionbasedoncascadingandclusteringofcorefigures AT chunrongzhang spammergroupdetectionbasedoncascadingandclusteringofcorefigures AT ningli spammergroupdetectionbasedoncascadingandclusteringofcorefigures AT dicksonkwchiu spammergroupdetectionbasedoncascadingandclusteringofcorefigures AT xianwenfang spammergroupdetectionbasedoncascadingandclusteringofcorefigures AT shujuanji spammergroupdetectionbasedoncascadingandclusteringofcorefigures |