  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.
