Abstract: Stance detection is a core task in social media public opinion analysis and plays a crucial role in understanding the distribution of public opinions. However, existing methods perform poorly in multi-turn dialogue scenarios, with a significant decline in modeling capability especially when dealing with deep-level comments. The main bottlenecks lie in the lack of a logical reasoning chain for implicit knowledge and the stance formation process, as well as insufficient target-dependent multi-granularity context modeling. To address these issues, this paper proposes a Chain-of-Thought enhanced Context Modeling method (CoT-CM) to improve the accuracy and robustness of stance detection in multi-turn dialogues. Leveraging the external knowledge of large language models, this method guides chain-of-thought reasoning through prompt design, extracts stance-related intermediate variables, and integrates them interactively with dialogue semantics, thereby depicting the reasoning process of the stance formation logic. Meanwhile, a multi-level dialogue semantic framework is designed to model the historical dialogue context from global, local, and relational perspectives, and a target-guided multi-hop attention mechanism is introduced to capture the most relevant information. In addition, a structural consistency contrastive learning mechanism is proposed, which effectively enhances the discriminative ability between different stances by jointly optimizing classification and contrastive losses. Experiments on Chinese multi-turn dialogue stance detection datasets C-MTCSD and ZS-CSD show that CoT-CM achieves an average F1 improvement of 2.97% and 1.36% respectively.

摘要： 立场检测是社交媒体舆情分析中的核心任务，对理解公众意见分布至关重要。然而，现有方法在多轮对话场景中表现不佳，尤其面对深层评论时建模能力显著下降。其主要瓶颈在于：缺乏对隐含知识与立场形成过程的逻辑推理链，以及对目标依赖的多粒度上下文建模。为此，提出了推理链增强的上下文建模方法（CoT-CM），以提升多轮对话立场检测的准确性与鲁棒性。该方法利用大语言模型的外部知识，通过提示设计引导链式推理，提取与立场相关的中间变量，并与对话语义交互融合，进而刻画立场形成逻辑的推理过程。同时，设计了多层次对话语义框架，从全局、局部和关系三个视角建模历史对话语境，并引入目标引导的多跳注意力机制以捕捉最相关信息。此外，提出结构一致性对比学习机制，通过联合优化分类与对比损失，有效增强不同立场的区分能力。在中文多轮对话立场检测数据集C-MTCSD和ZS-CSD上的实验表明，CoT-CM平均F1提升2.97%和1.36%。