Enhancing Stance Detection with Target-Stance Graph and Logical Reasoning

Stance detection identifies the attitude or stance toward specific targets and has a wide range of applications across various domains. Implicit mention of the target makes it difficult to establish connections between the target and the text. Existing approaches focus on integrating external knowledge but overlook the complex associations and stance information within it, which is crucial for maintaining reasoning consistency. To solve this problem, we propose a logical stance detection framework based on the principle of stance transitivity. Our framework achieves stance reasoning by leveraging symbolic and natural language reasoning. Specifically, we extract a list of targets related to the specific target from unlabeled data and use LLMs to construct a target-stance graph. This allows us to examine complicated interactions between targets and integrate stance information across related targets. We conducted massive experiments to validate the effectiveness of our proposed method. The experimental results indicate that our framework significantly improves the performance of stance detection tasks, offering a robust solution to the challenges posed by implicit targets.