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：社交媒体在人们日常生活中的突出地位，使其更倾向于通过社交网络而非传统渠道获取新闻。这种公众行为的转变为部分人在社交媒体上散布虚假新闻提供了可乘之机，进而引发负面经济、政治和社会后果，并导致公众信任缺失。现有诸多谣言检测方法大多未能充分利用新闻传播网络的异质性特征。为此，我们以先前提出的架构为基准，提出基于元路径嵌入的异质谣言传播结构特征提取方法，将其命名为"基于元路径的全局-局部注意力网络"（MGLAN）。在三个前沿数据集上的大量实验分析表明，MGLAN通过捕获节点级判别特征，实现了对不同类型节点的有效区分，性能优于其他模型。