摘要
[目的/意义]网络谣言传播中关键节点的检测对维护网络空间清朗、促进社会稳定具有重要意义。针对当前关键节点检测研究忽略节点在多条谣言传播过程中的作用,从而导致关键节点误检或漏检的问题,提出引入有向联合图的谣言传播中关键节点群组检测方法。[方法/过程]首先,通过整合多条谣言传播树,构建有向谣言传播联合图,获得涵盖多条谣言传播的网络结构图。其次,在该图中挖掘出节点发布谣言数量、传播谣言数量以及影响力三维指标,实现对图中节点重要性的量化。最后,通过计算依据重要性排序的节点谣言信息覆盖率,检测出谣言传播中的关键节点群组。[结果/结论]实证研究在公开数据集上展开,通过对数据集进行标注和可视化处理,构建了基于数据集的有向谣言传播联合图。实验结果显示:与已有方法相比,所提方法在准确率、召回率和F1值上均有所提高。
[Purpose/Significance]The detection of key nodes in the spread of online rumors is of great significance for maintaining a clear cyberspace and promoting social stability.Addressing the issue of current research on key node detection overlooking the significance of nodes in the dissemination of multiple rumors,which often leads to false or missed detection of key nodes.This paper introduces a method for detecting key node groups in rumor propagation using a directed joint graph.[Method/Process]Firstly,by integrating the propagation trees of multiple rumors,a directed rumor propagation joint graph was constructed,providing a network structure diagram encompassing the spread of various rumors.Secondly,by analyzing the three-dimensional indicators of rumor publication,spread,and influence of nodes in the graph,this paper could quantify the importance of nodes.Finally,by calculating the coverage of node rumor information sorted by importance,key node groups in rumor propagation were detected.[Result/Conclusion]Empirical research is conducted on public datasets.Annotating and visualizing the dataset constructs a directed rumor propagation joint graph.The empirical results show that compared to the existing methods,the proposed method has a certain improvement in Precision,Recall and F1 value.
作者
吴树芳
常欢
刘畅
张雄涛
Wu Shufang;Chang Huan;Liu Chang;Zhang Xiongtao(College of Management,Hebei University,Baoding 071000,China;Affiliated Hospital of Hebei University,Baoding 071000,China)
出处
《现代情报》
北大核心
2025年第9期97-107,共11页
Journal of Modern Information
基金
国家社会科学基金一般项目“跨平台网络敏感信息引导式检测及治理研究”(项目编:24BTQ057)。
关键词
谣言传播
有向谣言传播联合图
关键节点群组
谣言信息覆盖率
检测
rumor propagation
directed rumor propagation joint graph
key node group
rumor information coverage
detection
