{
  "annotation_id": "f12a0dd8-12f8-4845-92e8-5bfd51d2c41d",
  "date_created": "2026-02-17T05:53:23.789000Z",
  "date_modified": "2026-02-17T05:53:23.789000Z",
  "file_hash": "ba9028e052da6080ca5d61aa5013215e1a5bd9f989edce90ee47d3404d080064",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.10142",
    "authors": [
      "Ruiran Su",
      "Janet B. Pierrehumbert",
      "Markus Leippold"
    ],
    "categories": [
      "cs.CE"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models",
    "url": "https://arxiv.org/abs/2601.10142",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1c61c112-bc74-4f92-a916-d94332350b53",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}