构建首个大规模中文多轮对话立场检测数据集，推动中文舆情分析研究
C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset
- 构建2.4万条微博多轮对话标注数据，规模为前序数据集的4.2倍
- 零样本场景下最佳模型仅达64.07% F1，对话越深性能越差
- 聚焦隐含立场识别难题，适合中文社会舆情与对话理解研究者
立场检测已成为分析社交媒体公众讨论的重要工具。当前方法在中文处理和多轮对话分析方面面临显著挑战。为此，我们提出C-MTCSD，这是目前最大的中文多轮对话立场检测数据集，包含来自新浪微博的24,264条精心标注实例，规模是现有唯一中文对话立场检测数据集的4.2倍。通过传统方法与大语言模型的综合评估发现，C-MTCSD具有高度复杂性：即使最先进的模型在极具挑战性的零样本设置下，F1分数也仅为64.07%，且随着对话深度增加，性能持续下降。传统模型在隐含立场识别上表现尤其不佳，F1分数低于50%。本工作为中文立场检测研究建立了新的基准，凸显了未来改进的巨大空间。
原文摘要 · Abstract (English)
Stance detection has become an essential tool for analyzing public discussions on social media. Current methods face significant challenges, particularly in Chinese language processing and multi-turn conversational analysis. To address these limitations, we introduce C-MTCSD, the largest Chinese multi-turn conversational stance detection dataset, comprising 24,264 carefully annotated instances from Sina Weibo, which is 4.2 times larger than the only prior Chinese conversational stance detection dataset. Our comprehensive evaluation using both traditional approaches and large language models reveals the complexity of C-MTCSD: even state-of-the-art models achieve only 64.07% F1 score in the challenging zero-shot setting, while performance consistently degrades with increasing conversation depth. Traditional models particularly struggle with implicit stance detection, achieving below 50% F1 score. This work establishes a challenging new benchmark for Chinese stance detection research, highlighting significant opportunities for future improvements.
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