Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang

TL;DR
This paper introduces GARD, a novel graph autoencoder model that captures semantic evolvement and propagation structure to improve rumor detection accuracy and enable earlier identification of rumors on social media.
Contribution
The paper proposes a semantic evolvement enhanced graph autoencoder that effectively learns local and global semantic changes, improving rumor detection and early warning capabilities.
Findings
Outperforms state-of-the-art methods on benchmark datasets
Achieves earlier rumor detection by capturing semantic evolvement
Enhances model robustness with a uniformity regularizer
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Complex Network Analysis Techniques
