计算机科学 ›› 2025, Vol. 52 ›› Issue (3): 277-286.doi: 10.11896/jsjkx.240100204
李劭, 蒋方婷, 杨鑫岩, 梁刚
LI Shao, JIANG Fangting, YANG Xinyan, LIANG Gang
摘要： 现有社交网络谣言检测方法大多将社交网络中的单个帖子视为检测目标,存在因数据量不足而导致的检测冷启动问题,影响检测性能。另外,现有方法没有对海量社交网络信息中与检测无关的信息进行过滤,导致检测时延较长,性能较差。在分析谣言的传播特征时,现有方法大多侧重于谣言传播过程中的静态特征,难以充分利用节点间的动态关系对复杂的传播过程进行表征,导致性能提升存在瓶颈。针对以上问题,文中提出了一种基于潜在热点话题和图注意力神经网络的谣言检测方法,该方法采用神经主题模型和潜在热点话题发现模型进行话题级别的谣言检测以克服冷启动问题,并设计了一个基于双向图注意力神经网络的检测模型TPC-BiGAT,分析谣言话题传播过程中的动态特征以进行谣言真实性检测。在3个公开数据集上进行了多次实验证明,该方法在准确率上较现有方法取得了3%~5%的显著提升,验证了所提方法的有效性。
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