ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims

arXiv cs.CL / 3/30/2026

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Key Points

  • ClimateCheck 2026 is a shared task focused on automatically verifying climate-related claims against scientific literature despite specialized evidence and diverse disinformation rhetoric.
  • The 2026 edition expands the 2025 effort with tripled training data, introduces a new disinformation narrative classification task, and runs January to February 2026 on the CodaBench platform.
  • Participating systems used dense retrieval pipelines, cross-encoder ensembles, and large language models with structured hierarchical reasoning to improve evidence matching and verification.
  • The evaluation framework includes standard metrics (Recall@K and Binary Preference) and an additional automated method to assess retrieval quality under incomplete annotations, revealing systematic metric biases.
  • Cross-task analysis suggests that different types of climate disinformation vary in how verifiable they are, informing how future fact-checking systems should be designed.

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.