
Conforti, C., Berndt, J., Pilehvar, M. T., Giannitsarou, C., Toxvaerd, F. and Collier, N.
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval
Findings of the Association for Computational Linguistics: EMNLP 2020
pp. 4086-4101 (2020)
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.
Author links: Chryssi Giannitsarou Flavio Toxvaerd
Publisher's Link: http://dx.doi.org/10.18653/v1/2020.findings-emnlp.365
Keynes Fund Project(s):
Mapping of Rumours and Information Diffusion (JHOQ)