Probing the discourses of climate change (CC)
What can automatic text mining reveal about CC communication?
In a series of interrelated student projects, spanning across several seminars and research modules in 2020/2021, we apply text mining methods to a variety of text corpora (some of which we built ourselves). The overarching goal is to identify patterns, opinions and arguments brought forward in different CC discourses.
- GerCCT: 12.000 pairs of German Climate Change Tweets, collected at DRL (see Schäfer/Stede 2020 below)
- NatSciCC: 490 editorials from Nature and Science (1966-2015), manually annotated by Hulme et al. 2018 for thematic framing categories. We built a digital version of the corpus.
- NYTAC: New York Times Annotated Corpus. We identified 10.000 articles related to CC.
- CMV-CC: A CC subset of the "Change My View" subreddit corpus compiled by Webis.
- Framing in NatSciCC: Following up on the work of Hulme et al. 2018, we analyze the linguistics of framing in editorials.
- Classifying the NatSciCC texts: Focusing on the problem of imbalanced data, we aim at automatically reconstructing the topic frame annotations by Hulme et al. 2018 (see Bracke 2020 below).
- Tracking CC in NYTAC: We use unsupervised methods to detect patterns in CC reporting in The New York Times (1987-2007).
- Argumentation in Twitter exchanges: We study various subtaks of argumentation mining on the GerCCT corpus.
- Glossary: In collaboration with Digitales Wörterbuch der Deutschen Sprache, we build a linguistically-oriented glossary of German climate change terms.
BSc students (Computational Linguistics): Stefan Behring, Niklas Bitomsky, Yannic Bracke, Luise Köhler, Lydia Körber, Polina Krasilnikova, Elena Kröner, Johanna Rockstroh
MSc students (Cognitive Systems): Luka Borec, Juliane Hanel, Julia Hansmeier, Neele Charlotte Kinkel, Rodrigo López, Isabelle Nguyen
PhD student: Robin Schäfer
International collaborators: Nic Badullovich (ANU Climate Change Institute, Canberra), Patrick Saint-Dizier (Univ. Paul Sabatier, Toulouse)
- Manfred Stede (firstname.lastname@example.org)
- Robin Schäfer and Manfred Stede. Annotation and detection of arguments in tweets. In Proceedings of the 7th Workshop on Argument Mining, 53–58. Online, December 2020. Association for Computational Linguistics. [Bibtex] [PDF]
- Yannic Bracke. Automatic text classification with imbalanced data: Building a frame classifier from a corpus of editorials. Unpublished B.Sc. Thesis, 2020. [Bibtex]