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.- Anthology ID:
- L16-1200
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 1258–1263
- Language:
- URL:
- https://aclanthology.org/L16-1200
- DOI:
- Cite (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).
- Cite (Informal):
- DART: a Dataset of Arguments and their Relations on Twitter (Bosc et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/L16-1200.pdf