Pdf Automated Fact Checking Of Climate Claims With Large Researchgate

npj Climate Action volume 4, Article number: 17 (2025) Cite this article Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs). To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning.

It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI’s role in environmental discussions and policy with reliable data. In the era of digital information abundance, the endeavor to counter climate misinformation has found a promising ally in artificial intelligence (AI). Research shows that engaging with an AI chatbot on climate change can significantly align public perception with scientific consensus1, highlighting the importance of ensuring that the large language models (LLMs) underpinning these systems are... Therefore, we ask how well we can embed scientific consensus into automated fact-checking.

To this end, we developed Climinator—an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. Climinator evaluates the veracity of climate statements and improves its verdicts with evidence-based and scientifically credible reasoning and references to relevant literature. Our vision is to use AI to catalyze a well-informed global climate dialog, enrich public discourse with scientific insights, and foster a more informed society ready to engage with climate challenges. Climinator serves as a first step in this direction. Platforms like Climate Feedback and Skeptical Science have made commendable efforts to involve climate scientists in volunteering their expertise and providing an essential service in addressing climate misinformation. These scientists voluntarily dedicate their time to giving concise science-based evaluations, including references, and delivering a final verdict on disputed claims.

Despite their valuable contributions, these efforts face significant challenges, including scalability and actuality. Hence, their impact is limited by the sheer volume of misinformation and skepticism in digital media, worsened by misinformation spreading more rapidly and widely than factual information2. As a response, automated fact-checking3,4 aims to debunk misinformation at scale using natural language processing methods. While automated fact-checking tools have improved, they struggle with complex claims due to a lack of detailed reasoning5,6,7, particularly in the domain of climate change8. To address this problem, we introduce an advanced framework that overcomes these limitations by integrating LLMs within a Mediator-Advocate model. Although recent work has explored the aggregation of different viewpoints using LLMs to build a general consensus9, we address real-world claim complexities and evidence controversies in a novel way10,11,12.

In particular, we introduce separate “Advocates,” each drawing on a distinct text corpus to represent a specific viewpoint, while a “Mediator” either asks follow-up questions or synthesizes these perspectives into a cohesive and balanced... Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs). To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning.

It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI's role in environmental discussions and policy with reliable data. Keywords: Climate sciences; Communication; Education; Environmental social sciences. Competing interestsThe authors declare no competing interests. Fig.

1. Performance in terms of accuracy… Fig. 1. Performance in terms of accuracy (averaged micro-F1) of various models in classifying Climate… Please enter the email address that the record information will be sent to.

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arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Climate+Tech launches an open source AI fact-checking system that combines multiple AI models to evaluate climate claims against scientific sources, achieving 85% agreement with expert consensus. The spread of climate misinformation poses a significant challenge in our digital age. Recent research highlights the scale of this problem:

To address this challenge, we’re excited to announce the launch of Climate+Tech FactChecker, an open source AI-powered system designed to fact-check climate claims at scale while maintaining scientific rigor and transparency. Related Solution: This tool is part of our broader initiative on AI tools for society. Learn more about our complete ecosystem of fact-checking and democratic discourse tools in “AI Tools for Society: Fact-Checking, Democracy & Climate Communication”. Our system’s unique approach mirrors how expert panels evaluate scientific claims. Multiple AI “advocates” examine evidence independently from different scientific sources, provide structured reasoning with citations, and a “mediator” AI reconciles their findings into a final verdict.

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