Detecting Stance in Media on Global Warming

TL;DR

This paper introduces a dataset and classifier for detecting stance in media on global warming, analyzing how different sides frame opinions and sources, revealing common and partisan linguistic patterns.

Contribution

The work presents the GWSD dataset, a BERT-based stance classifier, and insights into opinion-framing strategies in global warming debates across media outlets.

Findings

Similar framing devices are used across GW-accepting and skeptic media.

GW-skeptical media shows more opponent-doubt in framing.

Authors often depict sources as hypocritical to support their stance.

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 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.