@inproceedings{conforti-etal-2020-will,
title = "Will-They-Won{'}t-They: A Very Large Dataset for Stance Detection on {T}witter",
author = "Conforti, Costanza and
Berndt, Jakob and
Pilehvar, Mohammad Taher and
Giannitsarou, Chryssi and
Toxvaerd, Flavio and
Collier, Nigel",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.157",
doi = "10.18653/v1/2020.acl-main.157",
pages = "1715--1724",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter
%A Conforti, Costanza
%A Berndt, Jakob
%A Pilehvar, Mohammad Taher
%A Giannitsarou, Chryssi
%A Toxvaerd, Flavio
%A Collier, Nigel
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F conforti-etal-2020-will
%X 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.
%R 10.18653/v1/2020.acl-main.157
%U https://aclanthology.org/2020.acl-main.157
%U https://doi.org/10.18653/v1/2020.acl-main.157
%P 1715-1724
Markdown (Informal)
[Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter](https://aclanthology.org/2020.acl-main.157) (Conforti et al., ACL 2020)
ACL