Abstract
Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual information and the propagation structure of events have been proposed. However, these methods overlook the importance of semantic evolvement information of event in propagation process, which is often challenging to be truly learned in supervised training paradigms and traditional rumor detection methods. To address this issue, we propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information through specific graph autoencoder and reconstruction strategies. By combining semantic evolvement information and propagation structure information, the model achieves a comprehensive understanding of event propagation and perform accurate and robust detection, while also detecting rumors earlier by capturing semantic evolvement information in the early stages. Moreover, in order to enhance the model's ability to learn the distinct patterns of rumors and non-rumors, we introduce a uniformity regularizer to further improve the model's performance. Experimental results on three public benchmark datasets confirm the superiority of our GARD method over the state-of-the-art approaches in both overall performance and early rumor detection.
Abstract (translated)
由于社交媒体上谣言的迅速传播，谣言检测已成为一个非常具有挑战性的任务。最近，许多利用文本信息和事件传播结构提出了很多谣言检测模型。然而，这些方法忽视了在传播过程中事件语义演变信息的重要性，而这种信息在监督训练范式和传统谣言检测方法中通常很难真正学习。为解决这个问题，我们提出了一个新颖的半监督演化增强图卷积神经网络（GARD）谣言检测模型，本文对其进行了阐述。该模型通过捕获局部语义变化和全局语义演化信息来学习事件语义演变信息，通过特殊的图卷积神经网络和重构策略获得全局语义演化信息。通过结合语义演变信息和传播结构信息，模型获得了对事件传播的全面理解，并能够准确和可靠地检测谣言，同时还能在谣言传播初期通过捕获语义演变信息来检测谣言。此外，为了增强模型学习不同谣言和非谣言的独特模式的能力，我们引入了均匀性正则化进一步改进了模型的性能。在三个公开基准数据集上的实验结果证实了我们在总体表现和早期谣言检测方面超过了最先进方法的优越性。
URL
https://arxiv.org/abs/2404.16076
