@inproceedings{bosc-etal-2016-dart,
title = "{DART}: a Dataset of Arguments and their Relations on {T}witter",
author = "Bosc, Tom and
Cabrio, Elena and
Villata, Serena",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1200",
pages = "1258--1263",
abstract = "The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.",
}
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%0 Conference Proceedings
%T DART: a Dataset of Arguments and their Relations on Twitter
%A Bosc, Tom
%A Cabrio, Elena
%A Villata, Serena
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F bosc-etal-2016-dart
%X The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.
%U https://aclanthology.org/L16-1200
%P 1258-1263
Markdown (Informal)
[DART: a Dataset of Arguments and their Relations on Twitter](https://aclanthology.org/L16-1200) (Bosc et al., LREC 2016)
ACL
- Tom Bosc, Elena Cabrio, and Serena Villata. 2016. DART: a Dataset of Arguments and their Relations on Twitter. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1258–1263, Portorož, Slovenia. European Language Resources Association (ELRA).