论文标题
在社交媒体上探索图形感知的多视图融合以进行谣言检测
Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media
论文作者
论文摘要
自动检测社交媒体上的谣言已成为一项艰巨的任务。先前的研究集中于从对话线程中学习指示性线索，以识别谣言信息。但是，这些方法仅模拟各种视图的谣言对话线程，但不能很好地融合多视图功能。在本文中，我们提出了一个新颖的多视图融合框架，用于谣言表示和分类。它基于图形卷积网络（GCN）编码多个视图，并利用卷积神经网络（CNN）捕获所有视图之间的一致和互补信息并将它们融合在一起。两个公共数据集的实验结果表明，我们的方法的表现优于最先进的方法。
Automatic detecting rumors on social media has become a challenging task. Previous studies focus on learning indicative clues from conversation threads for identifying rumorous information. However, these methods only model rumorous conversation threads from various views but fail to fuse multi-view features very well. In this paper, we propose a novel multi-view fusion framework for rumor representation learning and classification. It encodes the multiple views based on Graph Convolutional Networks (GCN), and leverages Convolutional Neural Networks (CNN) to capture the consistent and complementary information among all views and fuse them together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.
