@inproceedings{bauwelinck-lefever-2020-annotating,
title = "Annotating Topics, Stance, Argumentativeness and Claims in {D}utch Social Media Comments: A Pilot Study",
author = "Bauwelinck, Nina and
Lefever, Els",
editor = "Cabrio, Elena and
Villata, Serena",
booktitle = "Proceedings of the 7th Workshop on Argument Mining",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.argmining-1.2",
pages = "8--18",
abstract = "One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss{'} kappa) and moderate agreement for claim labelling (0.45 Fleiss{'} kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.",
}
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%0 Conference Proceedings
%T Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study
%A Bauwelinck, Nina
%A Lefever, Els
%Y Cabrio, Elena
%Y Villata, Serena
%S Proceedings of the 7th Workshop on Argument Mining
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F bauwelinck-lefever-2020-annotating
%X One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.
%U https://aclanthology.org/2020.argmining-1.2
%P 8-18
Markdown (Informal)
[Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study](https://aclanthology.org/2020.argmining-1.2) (Bauwelinck & Lefever, ArgMining 2020)
ACL