Detecting Stance in Media On Global Warming

Yiwei Luo, Dallas Card, Dan Jurafsky


Abstract
Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, “Leading scientists agree that global warming is a serious concern,” framing a clause which affirms their own stance (“that global warming is serious”) as an opinion endorsed ("[scientists] agree”) by a reputable source (“leading”). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: “Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other’s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author’s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.
Anthology ID:
2020.findings-emnlp.296
Original:
2020.findings-emnlp.296v1
Version 2:
2020.findings-emnlp.296v2
Version 3:
2020.findings-emnlp.296v3
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3296–3315
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.296
DOI:
10.18653/v1/2020.findings-emnlp.296
Bibkey:
Cite (ACL):
Yiwei Luo, Dallas Card, and Dan Jurafsky. 2020. Detecting Stance in Media On Global Warming. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3296–3315, Online. Association for Computational Linguistics.
Cite (Informal):
Detecting Stance in Media On Global Warming (Luo et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.296.pdf
Code
 yiweiluo/DeSMOG
Data
DeSMOG