Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection

Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang

Keywords: Rumor Detection, Social Media, Graph Autoencoder, Graph Representation

TL;DR: Deep learning for rumor detection on social media.

Abstract:

Due to the rapid spread of rumors on social media, which can cause widespread harm to the Web and society, rumor detection has become an extremely important challenge.
Recently, numerous rumor detection models have been proposed. They utilize textual information and the propagation structure of events. Some methods also introduce contrastive learning to enhance the robustness of model.
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 those contrastive learning based 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.

Track: Web Mining and Content Analysis

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Submission Number: 1015