计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240200116-8.doi: 10.11896/jsjkx.240200116
林熠笛, 李弼程, 杨海君
LIN Yidi, LI Bicheng, YANG Haijun
摘要： 社交媒体的快速崛起引发了谣言传播问题,对社会造成负面影响。现有的谣言检测算法主要关注新闻内容和传播结构,却往往忽视了用户偏好的相似性可能带来的潜在影响。在浏览帖文时,用户更容易接触到与自己具有相似偏好的其他用户所传播的信息,从而助长谣言的传播。此外,现有研究常常忽视了传播结构的多样性,也忽视了新闻内容与其传播结构之间的关联。不同类型的新闻应具备不同的传播模式。因此,本文提出了一种名为“SEPS”的模型,旨在在偏好相似用户之间建立联系,再将传播结构划分为多种形式,以提取不同传播模式的特征。最后,引入对比学习和共注意力模块,增强了新闻内容和传播结构之间的相关性。实验证明,“SEPS”模型能有效检测谣言,其性能超越了最佳基线模型。
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