Skip to Main content Skip to Navigation
New interface
Conference papers

DART: a Dataset of Arguments and their Relations on Twitter

Tom Bosc 1, * Elena Cabrio 1 Serena Villata 1 
* Corresponding author
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Elena Cabrio Connect in order to contact the contributor
Submitted on : Wednesday, June 15, 2016 - 4:27:12 PM
Last modification on : Thursday, August 4, 2022 - 4:54:57 PM


Files produced by the author(s)


  • HAL Id : hal-01332336, version 1



Tom Bosc, Elena Cabrio, Serena Villata. DART: a Dataset of Arguments and their Relations on Twitter. Proceedings of the 10th edition of the Language Resources and Evaluation Conference, May 2016, Portoroz, Slovenia. pp.1258-1263. ⟨hal-01332336⟩



Record views


Files downloads