论文标题
通过层次图注意，关于新闻的假新闻检测
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention
论文作者
论文摘要
假新闻的病毒传播造成了巨大的社会伤害，这使得假新闻发现成为紧迫的任务。当前的假新闻检测方法通过学习提取的新闻内容或内部知识的写作方式，在很大程度上依赖文本信息。但是，故意的谣言可以掩盖写作风格，绕过语言模型并使简单的基于文本的模型无效。实际上，新闻文章和其他相关组件（例如新闻创建者和新闻主题）可以建模为异质信息网络（简称HIN）。在本文中，我们提出了一个新颖的假新闻检测框架，即层次图形注意网络（HGAT），该框架使用一种新颖的分层注意机制在HIN中执行节点表示学习，然后通过分类新闻文章节点来检测假新闻。两个现实世界中的假新闻数据集的实验表明，HGAT可以胜过基于文本的模型和其他基于网络的模型。此外，该实验证明了我们用于图表表示学习和其他节点分类在异质图中的应用的可扩展性和概括性。
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components (such as news creators and news topics) can be modeled as a heterogeneous information network (HIN for short). In this paper, we propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes. Experiments on two real-world fake news datasets show that HGAT can outperform text-based models and other network-based models. In addition, the experiment proved the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.
