BiMGCL: rumor detection via bi-directional multi-level graph contrastive learning.

ABSTRACT: The rapid development of large language models has significantly reduced the cost of producing rumors, which brings a tremendous challenge to the authenticity of content on social media. Therefore, it has become crucially important to identify and detect rumors. Existing deep learning methods usually require a large amount of labeled data, which leads to poor robustness in dealing with different types of rumor events. In addition, they neglect to fully utilize the structural information of rumors, resulting in a need to improve their identification and detection performance. In this article, we propose a new rumor detection framework based on bi-directional multi-level graph contrastive learning, BiMGCL, which models each rumor propagation structure as bi-directional graphs and performs se

SUBMITTER: Feng W 

PROVIDER: S-EPMC10703058 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature