@inproceedings{abu-ahmad-etal-2025-climatecheck-shared,
    title = "The {C}limate{C}heck Shared Task: Scientific Fact-Checking of Social Media Claims about Climate Change",
    author = "Abu Ahmad, Raia  and
      Usmanova, Aida  and
      Rehm, Georg",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sdp-1.24/",
    doi = "10.18653/v1/2025.sdp-1.24",
    pages = "263--275",
    ISBN = "979-8-89176-265-7",
    abstract = "Misinformation in public discourse on global and significant issues like climate change is often facilitated through social media. However, current systems do not address fact-checking climate-related claims against trustworthy, evidence-based sources, such as scientific publications. We organised the ClimateCheck shared task at the 5th Scholarly Document Processing (SDP) Workshop, co-located with ACL 2025 in Vienna, Austria. The task featured two subtasks: 1. Abstracts retrieval given a claim, and 2. Claim verification based on the retrieved abstract. ClimateCheck had 27 registered users with active participation from 13 teams, ten of which submitted results for the first subtask and three for the second. The winning team achieved a Recall@10 score of 0.66 and a Binary Preference score of 0.49 for subtask I, and an F1 score of 0.73 for subtask II. Their method combined sparse retrieval using BM25, an ensemble of fine-tuned cross-encoder models using BGE-rerankers, and large language models for classification."
}