@inproceedings{luo-etal-2020-detecting,
    title = "Detecting Stance in Media On Global Warming",
    author = "Luo, Yiwei  and
      Card, Dallas  and
      Jurafsky, Dan",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.findings-emnlp.296/",
    doi = "10.18653/v1/2020.findings-emnlp.296",
    pages = "3296--3315",
    abstract = "Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, {\textquotedblleft}Leading scientists agree that global warming is a serious concern,{\textquotedblright} framing a clause which affirms their own stance ({\textquotedblleft}that global warming is serious{\textquotedblright}) as an opinion endorsed (''[scientists] agree{\textquotedblright}) by a reputable source ({\textquotedblleft}leading{\textquotedblright}). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: {\textquotedblleft}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."
}