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        <title>Modeling Discourse Profiles in Climate Misinformation Discourse</title>
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    <abstract>Prior work on climate misinformation detection typically classifies individual comments or claims using text alone. This framing overlooks a key property of online climate discourse: misinformation is often produced, reinforced, and sustained through recurring rhetorical patterns that reappear across comments and threads. Using a large-scale TikTok climate discourse dataset, we construct discourse profiles by clustering climate-related comments into recurring rhetorical patterns. We then incorporate these profiles into fine-grained climate misinformation classifiers using a late-fusion architecture. Under video-disjoint evaluation across three training seeds, discourse-profile-aware models significantly improve macro-F1 over text-only baselines for bag-of-words, TF-IDF, and MPNet representations. RoBERTa-large exhibits a positive mean improvement but substantially greater variability, and the difference is not statistically significant. Beyond classification, our results show discourse profiles shape misinformation reception and propagation. Discourse-profile-conditioned language model agents exhibit misinformation acceptance patterns aligned with profile-level misinformation prevalence in ClimateDisc, and the relationship between misinformation prevalence and engagement persistence differs across dominant discourse profiles.</abstract>
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%0 Conference Proceedings
%T Modeling Discourse Profiles in Climate Misinformation Discourse
%A Gao, Ge
%A Shan, Zhengyang
%A Plummer, Bryan A.
%A Betke, Margrit
%A Wijaya, Derry Tanti
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F gao-etal-2026-modeling
%X Prior work on climate misinformation detection typically classifies individual comments or claims using text alone. This framing overlooks a key property of online climate discourse: misinformation is often produced, reinforced, and sustained through recurring rhetorical patterns that reappear across comments and threads. Using a large-scale TikTok climate discourse dataset, we construct discourse profiles by clustering climate-related comments into recurring rhetorical patterns. We then incorporate these profiles into fine-grained climate misinformation classifiers using a late-fusion architecture. Under video-disjoint evaluation across three training seeds, discourse-profile-aware models significantly improve macro-F1 over text-only baselines for bag-of-words, TF-IDF, and MPNet representations. RoBERTa-large exhibits a positive mean improvement but substantially greater variability, and the difference is not statistically significant. Beyond classification, our results show discourse profiles shape misinformation reception and propagation. Discourse-profile-conditioned language model agents exhibit misinformation acceptance patterns aligned with profile-level misinformation prevalence in ClimateDisc, and the relationship between misinformation prevalence and engagement persistence differs across dominant discourse profiles.
%U https://aclanthology.org/2026.findings-aacl.146/
%P 2867-2887

Markdown (Informal)

[Modeling Discourse Profiles in Climate Misinformation Discourse](https://aclanthology.org/2026.findings-aacl.146/) (Gao et al., Findings 2026)

• Modeling Discourse Profiles in Climate Misinformation Discourse (Gao et al., Findings 2026)

ACL

• Ge Gao, Zhengyang Shan, Bryan A. Plummer, Margrit Betke, and Derry Tanti Wijaya. 2026. Modeling Discourse Profiles in Climate Misinformation Discourse. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 2867–2887, Hengqin, China. Association for Computational Linguistics.