With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of natural language processing (NLP). This study addresses the problem of detecting fake news on social media, which poses a significant challenge to society. This study proposes a new approach named GANM for fake news detection that employs NLP techniques to encode nodes for news context and user content and uses three graph convolutional networks to extract features and aggregate users' endogenous and exogenous information. The GANM employs a unique global attention mechanism with memory to learn the structural homogeneity of news dissemination networks. The approach achieves good results on a real dataset.

翻译：随着社交媒体的普及，虚假新闻检测已成为对社会构成重大威胁的关键问题。虚假信息的传播可能导致社会危害并损害信息的可信度。为解决这一问题，深度学习，尤其是自然语言处理技术的发展，已成为一种有前景的方法。本研究针对社交媒体上的虚假新闻检测问题展开，该问题对社会构成重大挑战。本文提出了一种名为GANM的新方法，该方法利用自然语言处理技术对新闻上下文和用户内容节点进行编码，并采用三个图卷积网络提取特征并聚合用户的内源性和外源性信息。GANM通过独特的带记忆的全局注意力机制学习新闻传播网络的结构同质性。该方法在真实数据集上取得了良好效果。