yiweiluo
commented
3 years ago Hi Matt--thanks so much for taking care of the title and PDF revisions!! I'm terribly sorry, but I forgot to specify that the abstract requires revision as well for consistency with the new dataset name (see below).
Metadata correction: please describe the issue here**
Change the abstract of the paper to the following:
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 Global Warming Stance Dataset (GWSD), 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.
Metadata correction: please describe the issue here**
Change the title of the paper to the following:
Revision or erratum: please add the following information**
Revised PDF
FindingsEMNLP20_luo_etal_revised.pdf
Description of changes
The acronym for the dataset introduced was revised in order to avoid confusion with the name of an existing organization.