@inproceedings{abu-ahmad-etal-2026-climatecheck,
    title = "{C}limate{C}heck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims",
    author = "Abu Ahmad, Raia  and
      Upravitelev, Max  and
      Usmanova, Aida  and
      Solopova, Veronika  and
      Rehm, Georg",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.nslp-1.6/",
    doi = "10.63317/3chh989hkiop",
    pages = "49--65",
    abstract = "Automatically verifying climate-related claims against scientific literature is a challenging task, complicated by the specialised nature of scholarly evidence and the diversity of rhetorical strategies underlying climate disinformation. ClimateCheck 2026 is the second iteration of a shared task addressing this challenge, expanding on the 2025 edition with tripled training data and a new disinformation narrative classification task. Running from January to February 2026 on the CodaBench platform, the competition attracted 20 registered participants and 8 leaderboard submissions, with systems combining dense retrieval pipelines, cross-encoder ensembles, and large language models with structured hierarchical reasoning. In addition to standard evaluation metrics (Recall@K and Binary Preference), we adapt an automated framework to assess retrieval quality under incomplete annotations, exposing systematic biases in how conventional metrics rank systems. A cross-task analysis further reveals that not all climate disinformation is equally verifiable, potentially implicating how future fact-checking systems should be designed."
}