Social spammers detection based on multi-view evidence Fusion

Zhang Donglin
Xu Jian
School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China

Abstract

To address the limitations of single-view spammer detection methods in processing complex and diverse social network data, and the issues of information loss and noise interference due to existing multi-view fusion methods not fully considering the quality differences between views, this study introduced a social spammer detection method based on Multi-View Evidence Fusion (MVEF) . The method integrated and analyzed three views: social relationships, behavioral characteristics, and tweet content, to extract key evidence. It employed Dirichlet distribution parameterization to assess the category credibility and overall uncertainty of each view in classification decisions. Through an efficient evidence fusion mechanism, the method skillfully utilized uncertainty to integrate key evidence from various views, constructing a comprehensive and reliable classification decision framework. Experimental results demonstrated that MVEF outperformed existing methods on two real-world Twitter datasets, effectively enhancing the accuracy and robustness of spammer detection.

Foundation Support

国防基础科研计划国防科技重点实验室稳定支持项目(WDZC20225250405)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.02.0039
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 10

Publish History

[2024-07-04] Accepted Paper

Cite This Article

张东林, 徐建. 基于多视图证据融合的社交水军检测 [J]. 计算机应用研究, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0039. (Zhang Donglin, Xu Jian. Social spammers detection based on multi-view evidence Fusion [J]. Application Research of Computers, 2024, 41 (10). (2024-07-12). https://doi.org/10.19734/j.issn.1001-3695.2024.02.0039. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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