@inproceedings{mascarell-etal-2021-stance,
title = "Stance Detection in {G}erman News Articles",
author = "Mascarell, Laura and
Ruzsics, Tatyana and
Schneebeli, Christian and
Schlattner, Philippe and
Campanella, Luca and
Klingler, Severin and
Kadar, Cristina",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2021",
address = "Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.fever-1.8",
doi = "10.18653/v1/2021.fever-1.8",
pages = "66--77",
abstract = "The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.",
}
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%0 Conference Proceedings
%T Stance Detection in German News Articles
%A Mascarell, Laura
%A Ruzsics, Tatyana
%A Schneebeli, Christian
%A Schlattner, Philippe
%A Campanella, Luca
%A Klingler, Severin
%A Kadar, Cristina
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F mascarell-etal-2021-stance
%X The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
%R 10.18653/v1/2021.fever-1.8
%U https://aclanthology.org/2021.fever-1.8
%U https://doi.org/10.18653/v1/2021.fever-1.8
%P 66-77
Markdown (Informal)
[Stance Detection in German News Articles](https://aclanthology.org/2021.fever-1.8) (Mascarell et al., FEVER 2021)
ACL
- Laura Mascarell, Tatyana Ruzsics, Christian Schneebeli, Philippe Schlattner, Luca Campanella, Severin Klingler, and Cristina Kadar. 2021. Stance Detection in German News Articles. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 66–77, Dominican Republic. Association for Computational Linguistics.