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Conference Papers Year : 2016

DART: a Dataset of Arguments and their Relations on Twitter

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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Dates and versions

hal-01332336 , version 1 (15-06-2016)

Identifiers

  • HAL Id : hal-01332336 , version 1

Cite

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⟩
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