The prominent role of social media in people's daily lives has made them more inclined to receive news through social networks than traditional sources. This shift in public behavior has opened doors for some to diffuse fake news on social media; and subsequently cause negative economic, political, and social consequences as well as distrust among the public. There are many proposed methods to solve the rumor detection problem, most of which do not take full advantage of the heterogeneous nature of news propagation networks. With this intention, we considered a previously proposed architecture as our baseline and performed the idea of structural feature extraction from the heterogeneous rumor propagation over its architecture using the concept of meta path-based embeddings. We named our model Meta Path-based Global Local Attention Network (MGLAN). Extensive experimental analysis on three state-of-the-art datasets has demonstrated that MGLAN outperforms other models by capturing node-level discrimination to different node types.
翻译:社交媒体在人们的日常生活中扮演着重要的角色,也使得他们更倾向于通过社交网络而不是传统媒体获取新闻。这种公众行为的转变为一些人传播虚假信息在社交媒体上打开了大门;其随之导致了负面的经济、政治和社会后果,以及公众之间的不信任。为解决谣言检测问题提出了许多方法,其中大多数方法没有充分利用新闻传播网络的异构性质。因此,我们以先前提出的体系结构为基准,并运用元路径嵌入的概念从异构谣言传播的结构特征提取出特征。我们将我们的模型命名为元路径全局本地注意网络(MGLAN)。在三个最先进的数据集上进行了广泛的实验分析,证明MGLAN能够通过捕捉节点级别的差异实现对不同节点类型的区分,从而优于其他模型。