STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval

Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier

Abstract
We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER). With its 3,291 expert-annotated articles, the dataset constitutes a high-quality benchmark for future research in SD and multi-task learning. We provide a detailed description of the corpus collection methodology and carry out an extensive analysis on the sources of disagreement between annotators, observing a correlation between their disagreement and the diffusion of uncertainty around a target in the real world. Our experiments show that the dataset poses a strong challenge to recent state-of-the-art models. Notably, our dataset aligns with an existing Twitter SD dataset: their union thus addresses a key shortcoming of previous works, by providing the first dedicated resource to study multi-genre SD as well as the interplay of signals from social media and news sources in rumour verification.
Anthology ID:
2020.findings-emnlp.365
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4086–4101
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.365
DOI:
10.18653/v1/2020.findings-emnlp.365
Bibkey:
Cite (ACL):
Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2020. STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4086–4101, Online. Association for Computational Linguistics.
Cite (Informal):
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval (Conforti et al., Findings 2020)
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https://preview.aclanthology.org/auto-file-uploads/2020.findings-emnlp.365.pdf
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 cambridge-wtwt/emnlp2020-stander-news
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