Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter
Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Abstract
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.- Anthology ID:
- 2020.acl-main.157
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1715–1724
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.157
- DOI:
- 10.18653/v1/2020.acl-main.157
- Cite (ACL):
- Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1715–1724, Online. Association for Computational Linguistics.
- Cite (Informal):
- Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter (Conforti et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.acl-main.157.pdf
- Code
- cambridge-wtwt/acl2020-wtwt-tweets + additional community code
- Data
- WT-WT