Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models

2026-01-15

AI Summary

This study addresses the long-standing lack of systematic analysis of discursive structures surrounding climate issues in financial news, which has hindered a nuanced understanding of climate investment contexts. It proposes an Actor-Frame-Argument (AFA) analytical framework, leveraging large language models (LLMs) to conduct a longitudinal discourse analysis of Dow Jones climate-related news coverage from 2000 to 2023, identifying key actors, thematic frames, and argumentative logics. The work innovatively establishes a reproducible, LLM-driven paradigm for media analysis and introduces a decomposed validation protocol, evaluating extraction outputs against human annotations (n=2,000) across dimensions of completeness, faithfulness, coherence, and relevance. Findings reveal a discursive shift: pre-2015 narratives emphasized climate risk and regulatory burden, whereas post-Paris Agreement discourse increasingly foregrounded economic opportunity and innovation, with financial institutions emerging as dominant narrative agents—highlighting a strategic reframing of the climate crisis by financial elites.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Financial news media shapes trillion-dollar climate investment decisions, yet discourse in this elite domain remains underexplored. We analyze two decades of climate-related articles (2000-2023) from Dow Jones Newswire using an Actor-Frame-Argument (AFA) pipeline that extracts who speaks, how issues are framed, and which arguments are deployed. We validate extractions against 2,000 human-annotated articles using a Decompositional Verification Framework that evaluates completeness, faithfulness, coherence, and relevance. Our longitudinal analysis uncovers a structural transformation: pre-2015 coverage emphasized risk and regulatory burden; post-Paris Agreement, discourse shifted toward economic opportunity and innovation, with financial institutions becoming dominant voices. Methodologically, we provide a replicable paradigm for longitudinal media analysis with LLMs; substantively, we reveal how financial elites have internalized and reframed the climate crisis across two decades.

Problem

• climate discourse
• financial news
• media analysis
• elite communication
• longitudinal study

Innovation

• Actor-Frame-Argument (AFA)
• Large Language Models
• Decompositional Verification Framework
• Longitudinal Media Analysis
• Climate Discourse