# Zero-Shot Conversational Stance Detection: Dataset and Approaches

arXiv: https://arxiv.org/abs/2506.17693 · HTML: https://mlsanity.com/paper/2506.17693/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [Multi-modal Stance Detection: New Datasets and Model](https://mlsanity.com/paper/2402.14298/) (2024)
- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://mlsanity.com/paper/2403.11145/) (2024)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://mlsanity.com/paper/2208.08797/) (2022)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations](https://mlsanity.com/paper/2006.00691/) (2020)
- [Multi-Granular Multimodal Clue Fusion for Meme Understanding](https://mlsanity.com/paper/2503.12560/) (2025)
- [Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model](https://mlsanity.com/paper/2409.00597/) (2024)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought](https://mlsanity.com/paper/2312.15291/) (2023)
- [Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge](https://mlsanity.com/paper/2506.15504/) (2025)

## Built on by (most cited first, 11 of 11)

- [LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces](https://mlsanity.com/paper/2602.14337/) (2026)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [Content Fuzzing for Escaping Information Cocoons on Digital Social Media](https://mlsanity.com/paper/2604.05461/) (2026)
- [Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.12518/) (2026)
- [Dynamic Emotion and Personality Profiling for Multimodal Deception Detection](https://mlsanity.com/paper/2604.17037/) (2026)
- [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://mlsanity.com/paper/2604.17255/) (2026)
- [ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks](https://mlsanity.com/paper/2605.00513/) (2026)
- [TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations](https://mlsanity.com/paper/2607.15240/) (2026)
- [StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting](https://mlsanity.com/paper/2607.24191/) (2026)
- [Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection](https://mlsanity.com/paper/2608.29066/) (2026)
- [MMDS-Bench: Benchmarking Multimodal Large Language Models on Dynamic Stance in Social Media Interactions](https://mlsanity.com/paper/2608.30903/) (2026)

## Abstract (arXiv)

Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the increasing number of online debates among social media users, conversational stance detection has become a crucial research area. However, existing conversational stance detection datasets are restricted to a limited set of specific targets, which constrains the effectiveness of stance detection models when encountering a large number of unseen targets in real-world applications. To bridge this gap, we manually curate a large-scale, high-quality zero-shot conversational stance detection dataset, named ZS-CSD, comprising 280 targets across two distinct target types. Leveraging the ZS-CSD dataset, we propose SITPCL, a speaker interaction and target-aware prototypical contrastive learning model, and establish the benchmark performance in the zero-shot setting. Experimental results demonstrate that our proposed SITPCL model achieves state-of-the-art performance in zero-shot conversational stance detection. Notably, the SITPCL model attains only an F1-macro score of 43.81%, highlighting the persistent challenges in zero-shot conversational stance detection.