A Natural Language Processing Approach to Identifying Partisan Framing of Climate Change Denialism, Fatalism, and Solutions in US Congressional Speeches
Bibliographic Data
| ID | 6330790 |
|---|---|
| Authors | Joseph Charles Van Matre (0000-0002-8051-2369, Ulster University Business School Belfast United Kingdom), Isabel L Krakoff (0000-0002-7153-059X, York University Toronto Ontario Canada), Allegra H Fullerton (0000-0003-2783-0824, Center for Policy and Democracy School of Public Affairs, University of Colorado Denver Denver Colorado USA) |
| Year | 2025 |
| Publication date | 2025-10-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Policy Studies Journal (JOURNAL) |
| Journal identifiers | ISSN: 0190-292X • E-ISSN: 1541-0072 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/psj.70079 |
| OpenAlex | W4414952275 |
| Language | EN |
| References cited | 42 |
This study examines the evolution of climate change discourse in the United States Congress from 1987 to 2017, employing natural language processing (NLP) techniques to analyze floor speeches. Using a la carte (ALC) word embeddings, we investigate how Democratic and Republican members of Congress frame climate change, focusing on denialist, fatalist, and solution‐oriented language. Our analysis reveals significant partisan divergences in climate change framing, with Republicans increasingly adopting denialist language, particularly around the term “global warming,” while Democrats maintain a more consistent, solution‐oriented approach. Both parties show a gradual increase in the use of fatalistic framing over time. These linguistic patterns reflect broader political strategies and evolving public discourse on climate change. By quantifying these semantic shifts, the findings contribute to the literature on agenda‐setting and policy framing offering a novel perspective on how political frames are constructed and maintained at the institutional level. This research not only enhances our understanding of climate change politics but also provides a methodological framework for analyzing long‐term trends in political discourse across various policy domains
Climate Change Communication and Perception · Computational and Text Analysis Methods · Social Media and Politics
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Embedding Regression
| Citation velocity | historical |
|---|---|
| Highly cited | No |