论文标题
使用异质社交媒体上下文图图探索假新闻检测图
Exploring Fake News Detection with Heterogeneous Social Media Context Graphs
论文作者
论文摘要
假新闻检测已成为一个超越纯粹学术兴趣的研究领域，因为它对我们整个社会具有直接的影响。最近的进步主要集中在基于文本的方法上。但是，很明显，要有效，需要整合其他上下文信息，例如新闻文章的传播行为以及在社交媒体上的用户互动模式。我们建议围绕新闻文章构建异质的社会背景图，并将问题重新制定为图形分类任务。探索包含不同类型的信息（以了解社会环境最有效的层次），并使用不同的图神经网络体系结构表明，这种方法在通用基准数据集中具有强大的结果非常有效。
Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. However, it has become clear that to be effective one needs to incorporate additional, contextual information such as spreading behaviour of news articles and user interaction patterns on social media. We propose to construct heterogeneous social context graphs around news articles and reformulate the problem as a graph classification task. Exploring the incorporation of different types of information (to get an idea as to what level of social context is most effective) and using different graph neural network architectures indicates that this approach is highly effective with robust results on a common benchmark dataset.
