Logic Augmented Multi-Decision Fusion Framework for Stance Detection on Social Media

Abstract

Stance detection in social media has become increasingly crucial for understanding public opinions on controversial issues. While large language models (LLMs) have shown promising results in stance detection, existing methods face challenges in reconciling inconsistent predictions and logical reasoning processes across different LLMs. To address these limitations, we propose LogiMDF, a Logic Augmented Multi-Decision Fusion framework that effectively integrates multiple LLMs’ decision processes through a unified logical framework. Our approach first employs zero-shot prompting to extract first-order logic (FOL) rules representing each LLM’s prediction rationale, then constructs a Logical Fusion Schema (LFS) to bridge different LLMs’ knowledge representations. We further develop a Multi-view Hypergraph Convolutional Network (MvHGCN) that effectively models and encodes the integrated logical knowledge. Extensive experiments on benchmark datasets demonstrate that LogiMDF significantly outperforms existing methods, achieving state-of-the-art performance in stance detection tasks. The results confirm that our framework effectively leverages the complementary strengths of multiple LLMs while maintaining consistent logical reasoning across different targets.