Multimodal Rumor Detection via Multi-Perspective Cross-Domain Hierarchical Fusion

Abstract

The proliferation of rumors on social media has grown exponentially in recent years. Existing approaches focus on training models on single-domain datasets, resulting in limited generalization in cross-domain scenarios. Additionally, multi-domain rumor detection faces two critical challenges: bridging semantic gaps across heterogeneous domains and addressing modality-dependent dependencies across them. To address these issues, we propose a Multi-domain Aware Network (MDAN) specifically for multimodal rumor detection. MDAN synergizes adaptive domain embeddings with a domain self-augmentation mechanism to dynamically select expert knowledge while incorporating a multi-level knowledge fusion module that hierarchically integrates domain-shared and domain-specific representations via gated attention networks. These dual knowledge optimization strategies effectively address the aforementioned challenges, significantly enhancing model robustness in multi-domain detection scenarios. Extensive experiments on two public datasets demonstrate that MDAN outperforms state-of-the-art methods in accuracy and cross-domain generalization.