Heterogeneous graph convolutional network for rumor detection with multi-level interactive fusion and graph reconstruction.

Early rumor detection on social media requires joint modeling of semantic content and dynamic propagation patterns, a critical yet challenging task in text mining. While existing methods often focus exclusively on either contextual information or user behavior, we propose MLI-GRA, a heterogeneous graph reconstruction approach that integrates both through multi-level interactive fusion. We first employ a graph auto-encoder framework to integrate semantic information and propagation patterns with the multiple graph convolutional network (GCN) and the graph reconstruction module. Then a multi-feature fusion module with adaptive gated fusion strategy is built to balance semantic and propagation features through multi-task learning.Experiments on real-world Twitter datasets demonstrate the superiority of our approach, achieving state-of-the-art (SOTA) results.