Rumor detection model with weighted GraphSAGE focusing on node location.

ABSTRACT: While social media platforms promote people's information exchange and dissemination, they also make rumors spread rapidly on online platforms. Therefore, how to detect rumors quickly, timely and accurately has become a hot topic for scholars in related fields. Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. To address these problems, we propose a location-aware weighted GraphSAGE rumor detection model GSMA. We first introduce an attention mechanism that dynamically ass

SUBMITTER: Ma M 

PROVIDER: S-EPMC11543811 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature