# C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset

arXiv: https://arxiv.org/abs/2504.09958 · HTML: https://mlsanity.com/paper/2504.09958/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)

## Built on by (most cited first, 5 of 5)

- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection](https://mlsanity.com/paper/2511.12130/) (2025)
- [A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection](https://mlsanity.com/paper/2604.26319/) (2026)
- [TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations](https://mlsanity.com/paper/2607.15240/) (2026)
- [Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection](https://mlsanity.com/paper/2608.29066/) (2026)

## Abstract (arXiv)

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.