Implicit Sign-Enhanced Stance Detection Model with Semantic Graph Attention Network

Chao Meng, Hongli Zhang, Gongzhu Yin, Yuchen Yang, Yang Gao, Binxing Fang

Abstract: Stance detection aims to automatically identify social users' attitudes on specific targets by analyzing their textual content and various relationships within social networks. Existing stance detection models predominantly focus on textual content. Although some stance detection methods based on graph neural networks have achieved performance improvements by integrating textual content and social relationships, they overlook the implicit polarity signs of links between nodes. Meanwhile, many real-world social networks do not provide explicit sign information for links, which limits the applicability of existing signed network representation learning methods to these scenarios. This paper proposes a novel social network stance detection model named 'Implicit Sign-enhanced Stance Detection Model with Semantic Graph Attention Network' (ISSDM-SemGAN). Firstly, we propose a semantic graph attention network (SemGAN) that leverages Direction-distance Fusion Scoring Function (DFSF) to enhance the differences between nodes, which lays the foundation for capturing implicit link signs. Additionally, we design implicit sign-enhanced stance detection network to introduce the signed link prediction task and social balance theory to facilitate understanding and learning the implicit link polarity between nodes, thereby enhancing the performance of stance detection tasks. Extensive experiments were conducted on real social network dataset. The experimental results demonstrate that ISSDM-SemGAN achieves state-of-the-art performance.

External IDs:dblp:conf/icc/MengZYYGF25