TRGCN: A Hybrid Framework for Social Network Rumor Detection

TL;DR

TRGCN is a hybrid model combining GCN and Transformer architectures to improve rumor detection accuracy on social networks by capturing structural and semantic features.

Contribution

The paper introduces a novel hybrid framework that integrates GCN and Transformer to simultaneously model structural and semantic information for rumor detection.

Findings

Significantly outperforms existing methods in accuracy on Twitter datasets.

Effectively captures long-range dependencies and sequence information among propagation nodes.

Demonstrates the superiority of the fusion model over standalone models.

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.