# Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter

arXiv: https://arxiv.org/abs/2005.00388 · HTML: https://mlsanity.com/paper/2005.00388/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 30 of 54)

- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Multi-modal Stance Detection: New Datasets and Model](https://mlsanity.com/paper/2402.14298/) (2024)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://mlsanity.com/paper/2403.11145/) (2024)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [Investigating label suggestions for opinion mining in German Covid-19 social media](https://mlsanity.com/paper/2105.12980/) (2021)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model](https://mlsanity.com/paper/2409.00597/) (2024)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [Can Large Language Models Address Open-Target Stance Detection?](https://mlsanity.com/paper/2409.00222/) (2024)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Unveiling the Cognitive Compass: Theory-of-Mind-Guided Multimodal Emotion Reasoning](https://mlsanity.com/paper/2602.00971/) (2026)
- [OpenStance: Real-world Zero-shot Stance Detection](https://mlsanity.com/paper/2210.14299/) (2022)
- [Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon](https://mlsanity.com/paper/2402.02113/) (2024)
- [MultiClimate: Multimodal Stance Detection on Climate Change Videos](https://mlsanity.com/paper/2409.18346/) (2024)
- [Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation](https://mlsanity.com/paper/2211.01467/) (2022)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization](https://mlsanity.com/paper/2402.01375/) (2024)
- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [Evolving Domain Adaptation of Pretrained Language Models for Text Classification](https://mlsanity.com/paper/2311.09661/) (2023)
- [Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions](https://mlsanity.com/paper/2109.00475/) (2021)
- [Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection](https://mlsanity.com/paper/2306.00765/) (2023)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [Semi-supervised Stance Detection of Tweets Via Distant Network Supervision](https://mlsanity.com/paper/2201.00614/) (2022)

## Abstract (arXiv)

We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection. Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain.