[1]荆军昌,张志勇,宋斌,等.融合用户传播风险和节点影响力分析的虚假信息传播控制方法[J].智能系统学报,2024,19(2):360-369.[doi:10.11992/tis.202210009]
JING Junchang,ZHANG Zhiyong,SONG Bin,et al.Disinformation diffusion control method integrating user propagation risk and node influence analysis[J].CAAI Transactions on Intelligent Systems,2024,19(2):360-369.[doi:10.11992/tis.202210009]
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JING Junchang,ZHANG Zhiyong,SONG Bin,et al.Disinformation diffusion control method integrating user propagation risk and node influence analysis[J].CAAI Transactions on Intelligent Systems,2024,19(2):360-369.[doi:10.11992/tis.202210009]
融合用户传播风险和节点影响力分析的虚假信息传播控制方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第2期
页码:
360-369
栏目:
学术论文—智能系统
出版日期:
2024-03-05
- Title:
- Disinformation diffusion control method integrating user propagation risk and node influence analysis
- Keywords:
- online social networks; disinformation; propagation risk; embedded representation; node influence; adaptive weighting; discrete particle swarm; diffusion control
- 分类号:
- TP309
- 文献标志码:
- 2024-01-05
- 摘要:
- 在线社交网络中虚假信息传播蔓延成为当前网络空间安全治理面临的重要挑战。提出一种融合用户传播风险和节点影响力分析的虚假信息传播控制方法DDC-UPRNI (disinformation diffusion control method integrating user propagation risk and node influence analysis)。综合考虑虚假信息传播特征空间的多样性和复杂性，通过自注意力机制实现用户传播虚假信息行为维度、时间维度和内容维度特征的嵌入表示，运用改进的无监督聚类K-means++算法实现不同用户传播风险等级的自动划分；设计一种自适应加权策略实现对离散粒子群优化算法的改进，进而提出一种基于离散粒子群优化的虚假信息传播关键节点选取方法，用于从具有特定传播风险等级的用户节点集合中选取若干个具有影响力的控制驱动节点，从而实现精准、高效的虚假信息传播控制；基于现实在线社交网络平台上开展试验，结果表明，所提出的DDC-UPRNI方法与现有算法相比，在控制效果和时间复杂度等重要指标上具有明显优势。该方法为社会网络空间中的虚假信息管控治理提供重要参考。
- Abstract:
- The spread of disinformation on online social networks (OSNs) has become a critical challenge for cyberspace security governance. This paper presents DDC-UPRNI, a disinformation diffusion control method, by integrating user propagation risk with node influence analysis. First, comprehensively considering the diversity and complexity of the characteristic space of disinformation propagation, an embedded representation of the behavior, time and content dimensions of user propagation of disinformation is realized through the self-attention mechanism, and the automatic classification of different user propagation risk levels is achieved using the improved unsupervised clustering K-means++ algorithm. Second, an adaptive weighting strategy is designed to improve the discrete particle swarm optimization algorithm, and a method for selecting key nodes of disinformation propagation is proposed based on the discrete particle swarm optimization. This method determines several influential control driving nodes from the user node set with a specific propagation risk level to achieve accurate and highly efficient disinformation propagation control. Finally, experiments are performed on a real OSN platform, and the results demonstrate that the proposed DDC-UPRNI method has obvious advantages over other existing algorithms in some important indicators, including control effect and time complexity. This method provides a significant reference value for the current governance of disinformation in social cyberspace.
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- 相似文献/References:
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备注/Memo
收稿日期:2022-10-09。
基金项目:国家自然科学基金项目（61972133）；河南省中原科技创新领军人才项目（204200510021）；中国博士后科学基金项目（2021M700885）
作者简介:荆军昌，博士研究生，中国计算机学会会员，主要研究方向为多媒体内容安全、社会计算与社会智能、大数据分析。E-mail：jingjunchang2012@126.com;张志勇，教授，博士生导师，博士，CCF/CAAI/IEEE/ACM高级会员，主要研究方向为网络空间安全、复杂社会网络分析、人工智能。主持和参与国家自然科学基金项目4项、教育部重点项目20余项。出版学术专著4部、编著2部和译著1部，发表学术论文 120余篇。E-mail：xidianzzy@126.com;宋斌，讲师，博士，中国计算机学会会员，CAAI高级会员，主要研究方向为人工智能、图像处理、社交网络安全。主持和参与河南省科技攻关等省级课题3项，出版学术专著3部，发表学术论文20余篇。E-mail：songbin@haust.edu.cn
通讯作者:张志勇. E-mail：xidianzzy@126.com
更新日期/Last Update:
1900-01-01