@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://preview.aclanthology.org/ingest-eacl/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."
}