@inproceedings{su-etal-2026-actors,
title = "Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models",
author = "Su, Ruiran and
Leippold, Markus and
Pierrehumbert, Janet B.",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.104/",
pages = "1994--2014",
ISBN = "979-8-89176-386-9",
abstract = "We curate a 980,061-article corpus of climate-related financial news from the Dow Jones Newswire (2000{--}2023) and introduce a three-stage Actor{--}Frame{--}Argument (AFA) pipeline that uses large language models to extract actors, stances, frames, and argumentative structures. We conduct AFA extraction on a stratified, uncertainty-enriched sample of 4,143 articles that preserves the temporal and thematic distributions of the full corpus. Reliability is established with a 2,000-article human-annotated gold standard and a Decompositional Verification Framework (DVF) that decomposes evaluation into completeness, faithfulness, coherence, and relevance, with multi-judge scoring calibrated against human ratings. Our longitudinal analysis uncovers a structural shift after 2015: coverage transitions from risk and regulatory-burden frames toward economic opportunity and technological innovation; financial institutions and companies increasingly deploy opportunity-centered arguments, while NGOs emphasize environmental urgency and governments stress compliance. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs. For high-stake domain insights, we map how the financial sector has internalized and reframed the climate crisis across two decades."
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<abstract>We curate a 980,061-article corpus of climate-related financial news from the Dow Jones Newswire (2000–2023) and introduce a three-stage Actor–Frame–Argument (AFA) pipeline that uses large language models to extract actors, stances, frames, and argumentative structures. We conduct AFA extraction on a stratified, uncertainty-enriched sample of 4,143 articles that preserves the temporal and thematic distributions of the full corpus. Reliability is established with a 2,000-article human-annotated gold standard and a Decompositional Verification Framework (DVF) that decomposes evaluation into completeness, faithfulness, coherence, and relevance, with multi-judge scoring calibrated against human ratings. Our longitudinal analysis uncovers a structural shift after 2015: coverage transitions from risk and regulatory-burden frames toward economic opportunity and technological innovation; financial institutions and companies increasingly deploy opportunity-centered arguments, while NGOs emphasize environmental urgency and governments stress compliance. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs. For high-stake domain insights, we map how the financial sector has internalized and reframed the climate crisis across two decades.</abstract>
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%0 Conference Proceedings
%T Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models
%A Su, Ruiran
%A Leippold, Markus
%A Pierrehumbert, Janet B.
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F su-etal-2026-actors
%X We curate a 980,061-article corpus of climate-related financial news from the Dow Jones Newswire (2000–2023) and introduce a three-stage Actor–Frame–Argument (AFA) pipeline that uses large language models to extract actors, stances, frames, and argumentative structures. We conduct AFA extraction on a stratified, uncertainty-enriched sample of 4,143 articles that preserves the temporal and thematic distributions of the full corpus. Reliability is established with a 2,000-article human-annotated gold standard and a Decompositional Verification Framework (DVF) that decomposes evaluation into completeness, faithfulness, coherence, and relevance, with multi-judge scoring calibrated against human ratings. Our longitudinal analysis uncovers a structural shift after 2015: coverage transitions from risk and regulatory-burden frames toward economic opportunity and technological innovation; financial institutions and companies increasingly deploy opportunity-centered arguments, while NGOs emphasize environmental urgency and governments stress compliance. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs. For high-stake domain insights, we map how the financial sector has internalized and reframed the climate crisis across two decades.
%U https://aclanthology.org/2026.findings-eacl.104/
%P 1994-2014
Markdown (Informal)
[Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models](https://aclanthology.org/2026.findings-eacl.104/) (Su et al., Findings 2026)
ACL