Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism

Shraey Bhatia, Jey Han Lau, Timothy Baldwin


Abstract
Neutralisation techniques, e.g. denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view. We first draw on social science to introduce the problem to the community of nlp, present the granularity of the coding schema and then collect manual annotations of neutralised techniques in text relating to climate change, and experiment with supervised and semi- supervised BERT-based models.
Anthology ID:
2021.naacl-main.175
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2167–2175
Language:
URL:
https://aclanthology.org/2021.naacl-main.175
DOI:
10.18653/v1/2021.naacl-main.175
Bibkey:
Cite (ACL):
Shraey Bhatia, Jey Han Lau, and Timothy Baldwin. 2021. Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2167–2175, Online. Association for Computational Linguistics.
Cite (Informal):
Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism (Bhatia et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.175.pdf
Video:
 https://aclanthology.org/2021.naacl-main.175.mp4