@inproceedings{evrard-etal-2020-french,
title = "{F}rench Tweet Corpus for Automatic Stance Detection",
author = "Evrard, Marc and
Uro, R{\'e}mi and
Herv{\'e}, Nicolas and
Mazoyer, B{\'e}atrice",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.775",
pages = "6317--6322",
abstract = "The automatic stance detection task consists in determining the attitude expressed in a text toward a target (text, claim, or entity). This is a typical intermediate task for the fake news detection or analysis, which is a considerably widespread and a particularly difficult issue to overcome. This work aims at the creation of a human-annotated corpus for the automatic stance detection of tweets written in French. It exploits a corpus of tweets collected during July and August 2018. To the best of our knowledge, this is the first freely available stance annotated tweet corpus in the French language. The four classes broadly adopted by the community were chosen for the annotation: support, deny, query, and comment with the addition of the ignore class. This paper presents the corpus along with the tools used to build it, its construction, an analysis of the inter-rater reliability, as well as the challenges and questions that were raised during the building process.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings
%T French Tweet Corpus for Automatic Stance Detection
%A Evrard, Marc
%A Uro, Rémi
%A Hervé, Nicolas
%A Mazoyer, Béatrice
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F evrard-etal-2020-french
%X The automatic stance detection task consists in determining the attitude expressed in a text toward a target (text, claim, or entity). This is a typical intermediate task for the fake news detection or analysis, which is a considerably widespread and a particularly difficult issue to overcome. This work aims at the creation of a human-annotated corpus for the automatic stance detection of tweets written in French. It exploits a corpus of tweets collected during July and August 2018. To the best of our knowledge, this is the first freely available stance annotated tweet corpus in the French language. The four classes broadly adopted by the community were chosen for the annotation: support, deny, query, and comment with the addition of the ignore class. This paper presents the corpus along with the tools used to build it, its construction, an analysis of the inter-rater reliability, as well as the challenges and questions that were raised during the building process.
%U https://aclanthology.org/2020.lrec-1.775
%P 6317-6322
Markdown (Informal)
[French Tweet Corpus for Automatic Stance Detection](https://aclanthology.org/2020.lrec-1.775) (Evrard et al., LREC 2020)
ACL
- Marc Evrard, Rémi Uro, Nicolas Hervé, and Béatrice Mazoyer. 2020. French Tweet Corpus for Automatic Stance Detection. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6317–6322, Marseille, France. European Language Resources Association.