TRGCN: A Hybrid Framework for Social Network Rumor Detection

2026-01-20

AI Summary

This work addresses the challenge that existing rumor detection methods struggle to simultaneously model sequential dependencies along propagation paths and global topological structures in social networks. To this end, the paper proposes a novel architecture that effectively integrates Graph Convolutional Networks (GCNs) with Transformers—a combination not previously achieved. The approach preserves temporal propagation information through positional encoding and leverages multi-head attention mechanisms to capture long-range dependencies and cross-subspace feature interactions among nodes. This enables joint modeling of rumor propagation structures, textual semantics, and topological relationships. Experimental results on the Twitter15 and Twitter16 datasets demonstrate that the proposed method significantly outperforms current state-of-the-art models, achieving notable improvements in accuracy.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technology, machine learning and deep learning approaches for rumor identification have gradually emerged and gained prominence. However, previous approaches often struggle to simultaneously capture both the sequential and the global structural relationships among topological nodes within a social network. To tackle this issue, we introduce a hybrid model for detecting rumors that integrates a Graph Convolutional Network (GCN) with a Transformer architecture, aiming to leverage the complementary strengths of structural and semantic feature extraction. Positional encoding helps preserve the sequential order of these nodes within the propagation structure. The use of Multi-head attention mechanisms enables the model to capture features across diverse representational subspaces, thereby enhancing both the richness and depth of text comprehension. This integration allows the framework to concurrently identify the key propagation network of rumors, the textual content, the long-range dependencies, and the sequence among propagation nodes. Experimental evaluations on publicly available datasets, including Twitter 15 and Twitter 16, demonstrate that our proposed fusion model significantly outperforms both standalone models and existing mainstream methods in terms of accuracy. These results validate the effectiveness and superiority of our approach for the rumor detection task.

Problem

• rumor detection
• social network
• sequential dependencies
• structural relationships
• graph neural networks

Innovation

• Graph Convolutional Network
• Transformer
• Rumor Detection
• Multi-head Attention
• Positional Encoding