Jointly multi-source information and local-global relations of heterogeneous network for rumor detection

Abstract

The widespread rumors on social media seriously disturb the social order, and we urgently need practical methods to detect rumors. Most existing deep learning methods focus on mining news text content, user information, and propagation features but ignore the rumor diffusion structural features. Rumors spread in a vertical chain and diffusion in a horizontal network. Both are essential features of rumors. In addition, existing models need more effective methods to extract higher-order features of multiple resource information. To address these problems, we propose a multi-source information heterogeneous graph model in this paper, called jointly Multi-Source information and Local-Global relationship of heterogeneous network model named MSLG. It extracts multi-source information such as rumors content, user information, propagation, and diffusion structure. Firstly, we extract the higher order semantic representation of rumors content by graph convolution network and integrate local relational attention to strengthen the critical semantic. At the same time, we construct the rumors and users as heterogeneous graphs to capture the propagation and diffusion structure of the rumors. We are finally fusing global relational attention to measure submodules’ importance. Experiments on two real-world datasets show that the proposed method achieves state-of-the-art results in fake news detection.