LQMF-RD: A lightweight quantum-driven multi-modal fusion framework for rumor detection

Abstract

In recent years, automated rumor detection has garnered significant attention. Despite notable progress in multi-modal modeling for social media rumor detection, two major challenges remain: (1) the dynamic characteristics of social networks during the propagation process are often overlooked; (2) multi-modal features (such as text, images, and propagation graphs), are often poorly aligned and lead to redundant model parameters. To address these issues, we propose LQMF-RD, a lightweight quantum-driven multi-modal feature fusion framework for rumor detection. First, to capture the dynamic nature of rumor propagation, we design a Dynamic Graph Network (DGN) that leverages the spatiotemporal characteristics of propagation graph, effectively capturing both neighborhood dependencies and temporal evolution among nodes. Then, we employ amplitude encoding to project the extracted multi-modal features into a $\left[{\log }_{2}N\right]$-dimensional quantum state space. Finally, we construct a Lightweight Quantum-driven Multi-modal Fusion Network (LQMFN), which enables deep interaction and fusion of multi-modal features through quantum convolution and pooling operations. LQMFN updates only 0.01M parameters, substantially reducing computational complexity and storage overhead. Experimental results show that LQMF-RD not only delivers superior performance on rumor detection tasks, but also achieves high computational efficiency and strong robustness to quantum noise.