Knowledge-Augmented Interpretable Network for Zero-Shot Stance Detection on Social Media

Stance detection on social media has become increasingly important for understanding public opinions on controversial issues. Existing methods often require large amounts of labeled data to learn target-independent transferable knowledge, which is infeasible under zero-shot settings where the target is unseen. Furthermore, most current stance detection models, primarily based on end-to-end deep learning architectures, lack transparency and may produce counter-intuitive and uninterpretable predictions. In this article, we propose a novel knowledge-augmented interpretable network (KAI) to enable zero-shot stance detection (ZSSD). First, we introduce an unsupervised approach based on large language models (LLM-KE) to elicit analysis perspectives, which is target-independent knowledge shared across different targets. This transferable knowledge bridges connections between seen and unseen targets. Second, we develop a bidirectional knowledge-guided neural production system (Bi-KGNPS) that effectively integrates such transferable knowledge through an iterative knowledge-variable binding process to guide stance predictions. Extensive experiments on benchmark datasets demonstrate KAI achieves new state-of-the-art performance on ZSSD. Moreover, our approach also delivers strong results on conventional in-target and cross-target stance detection. With the dual benefits of knowledge-augmented accuracy and model interpretability, this work represents an important advance toward practical stance detection systems that can generalize to emerging topics of interest. The proposed KAI framework provides an interpretable approach to effectively transfer knowledge across domains for zero-shot learning.