Author Identifier

Zoe Leviston: https://orcid.org/0000-0002-4969-7916

Document Type

Journal Article

Publication Title

Scientific Reports

Volume

15

Issue

1

PubMed ID

40263334

Publisher

Nature

School

School of Arts and Humanities

RAS ID

82041

Funders

Australian National University / Melbourne School of Psychological Sciences at the University of Melbourne / Australian Research Council

Grant Number

ARC Number : DE240100001

Comments

Lee, A. S., Kirkland, K., Stanley, S. K., Robinson, A., Leviston, Z., & Walker, I. (2025). A thematic analysis of what Australians state would change their minds on climate change. Scientific Reports, 15, 12989. https://doi.org/10.1038/s41598-025-96714-z

Abstract

What do Australians believe would change their current opinions about climate change? In this study, we used audience segmentation analysis through the Six Americas Short Survey to identify groups of climate opinion holders within a representative sample of Australians. We had 4857 participants tell us what it would take to change their current opinions about climate change and leveraged OpenAI’s Generative Pre-Trained Transformer (GPT) to identify the presence or absence of themes (Nothing, Evidence and Information, Trusted Sources, Action, and Unsure) and subthemes in their responses. GPT performed at near-human levels, proving to be a highly useful tool for thematic analysis. Our analyses revealed that strong climate denialists and believers tended to display greater dogmatism, with increased likelihood of stating that nothing would change their mind and lower likelihood of being unsure. Results also highlighted the need for diverse forms of evidence and information and the importance of trusted sources of information across audience segments. These findings provide support for GPT’s utility in managing large datasets in the social sciences and offer participant-informed insights into climate opinion change.

DOI

10.1038/s41598-025-96714-z

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Link to publisher version (DOI)

10.1038/s41598-025-96714-z