# Detecting Stance in Media on Global Warming

arXiv: https://arxiv.org/abs/2010.15149 · HTML: https://mlsanity.com/paper/2010.15149/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 30 of 48)

- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Can Unconfident LLM Annotations Be Used for Confident Conclusions?](https://mlsanity.com/paper/2408.15204/) (2024)
- [chatClimate: Grounding Conversational AI in Climate Science](https://mlsanity.com/paper/2304.05510/) (2023)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://mlsanity.com/paper/2304.00815/) (2023)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP](https://mlsanity.com/paper/2601.09065/) (2026)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://mlsanity.com/paper/2112.07475/) (2021)
- [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022)
- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?](https://mlsanity.com/paper/2305.06074/) (2023)
- [Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia](https://mlsanity.com/paper/2410.04282/) (2024)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Voices in a Crowd: Searching for Clusters of Unique Perspectives](https://mlsanity.com/paper/2407.14259/) (2024)
- [Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text](https://mlsanity.com/paper/2409.02078/) (2024)
- [Leveraging Annotator Disagreement for Text Classification](https://mlsanity.com/paper/2409.17577/) (2024)
- [Probably Approximately Correct Labels](https://mlsanity.com/paper/2506.10908/) (2025)
- [Valid Inference with Imperfect Synthetic Data](https://mlsanity.com/paper/2508.06635/) (2025)
- [Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance](https://mlsanity.com/paper/2403.00236/) (2024)
- [Measuring and Addressing Indexical Bias in Information Retrieval](https://mlsanity.com/paper/2406.04298/) (2024)
- [Automatic Claim Review for Climate Science via Explanation Generation](https://mlsanity.com/paper/2107.14740/) (2021)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [Unlearning Climate Misinformation in Large Language Models](https://mlsanity.com/paper/2405.19563/) (2024)
- [Talking Point based Ideological Discourse Analysis in News Events](https://mlsanity.com/paper/2504.07400/) (2025)
- [NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action](https://mlsanity.com/paper/2105.05621/) (2021)
- [Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging](https://mlsanity.com/paper/2305.06174/) (2023)

## Abstract (arXiv)

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.