Abstract
While the field of argument mining has grown notably in the last decade, research on the Twitter medium remains relatively understudied. Given the difficulty of mining arguments in tweets, recent work on creating annotated resources mainly utilized simplified annotation schemes that focus on single argument components, i.e., on claim or evidence. In this paper we strive to fill this research gap by presenting GerCCT, a new corpus of German tweets on climate change, which was annotated for a set of different argument components and properties. Additionally, we labelled sarcasm and toxic language to facilitate the development of tools for filtering out non-argumentative content. This, to the best of our knowledge, renders our corpus the first tweet resource annotated for argumentation, sarcasm and toxic language. We show that a comparatively complex annotation scheme can still yield promising inter-annotator agreement. We further present first good supervised classification results yielded by a fine-tuned BERT architecture.- Anthology ID:
- 2022.lrec-1.658
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6121–6130
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.658
- DOI:
- Cite (ACL):
- Robin Schaefer and Manfred Stede. 2022. GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6121–6130, Marseille, France. European Language Resources Association.
- Cite (Informal):
- GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change (Schaefer & Stede, LREC 2022)
- PDF:
- https://preview.aclanthology.org/autopr/2022.lrec-1.658.pdf