原标题：Rumor Detection on Social Media withBi-Directional Graph Convolutional Networks
作者：Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao,Wen bing Huang, Yu Rong, Junzhou Huang
关键词：Rumor Detection; GCN; Social Media
中文摘要：
本文解决的是社交媒体上的谣言检测问题。已有一些研究使用深度学习的方法，通过谣言的传播方式检测出谣言。例如RvNN（Recursive Neural Network）。但是这些深度学习方法在谣言检测中只考虑到了传播的深度，却忽视了广泛散布（wide dispersion）的结构。实际上传播（propagation）和散布（dispersion）是谣言的两个关键特征。本文提出Bi-GCN（Bi-Directional Graph Convolutional Networks）图模型，从谣言自顶向下（top-down）和自底向上（bottom-up）的传播方向上发掘这两个特征。模型中使用到了两个GCN，一个GCN使用了谣言传播的top-down的有向图以学习到谣言的传播模式；另一个GCN使用了相反的有向图以捕获到谣言的散布模式。此外，GCN的每一层中都利用到了源帖子的信息，以增强谣言根源的影响。作者在一些benchmarks上进行实验，结果显示本文的方法实现了SOTA。
英文摘要：
Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.
原文链接：https://arxiv.org/pdf/2001.06362v1.pdf
文献总结：
