@inproceedings{luo2020desmog,
 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.},
 address = {Online},
 author = {Luo, Yiwei  and
Card, Dallas  and
Jurafsky, Dan},
 booktitle = {Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing (EMNLP) 2020},
 link = {https://www.aclweb.org/anthology/2020.findings-emnlp.296},
 month = {nov},
 pages = {3296--3315},
 publisher = {Association for Computational Linguistics},
 title = {{D}e{SMOG}: Detecting Stance in Media On Global Warming},
 url = {https://nlp.stanford.edu/pubs/luo2020desmog.pdf},
 year = {2020}
}