FACTors：一个用于研究事实核查生态系统的新数据集
FACTors: A New Dataset for Studying the Fact-checking Ecosystem
AI总结：
本文提出 FACTors 数据集，覆盖1995—2025年39个合规事实核查组织的报告与声明，支持生态系统层面分析，并展示统计、倾向与可信度评分等应用。
摘要：
我们打击虚假信息的斗争由事实核查人员牵头。他们调查声明的真实性，并将调查结果记录为事实核查报告。随着在线传播的虚假信息量迅速增加，在事实核查流程中使用自动化，旨在通过增强可扩展性来强化这一生态系统。包含已核查声明的数据集在开发此类自动化解决方案方面发挥着关键作用。然而，据我们所知，目前还没有生态系统层面的事实核查数据集，能够覆盖足够长时期内的声明，并来源广泛、反映整个生态系统中的各类参与者，且该生态系统公认遵循被广泛接受的事实核查准则和原则。我们提出了一个新数据集 FACTors，这是首个通过提供事实核查的生态系统层面数据来填补这一空白的数据集。它包含来自117,993份英文事实核查报告的118,112条声明，这些报告由1,953名个人（共同）撰写，并于1995—2025年期间由39个事实核查组织发布；这些组织是 IFCN（International Fact-Checking Network，国际事实核查网络）和/或 EFCSN（European Fact-Checking Standards Network，欧洲事实核查标准网络）的活跃签署方。数据集包含7,327条由多个事实核查组织调查的重叠声明，对应2,977条唯一声明。它支持对事实核查者（组织和个人）开展新的生态系统层面研究。为证明 FACTors 的实用性，我们展示了三个示例应用，包括一项同类首创的事实核查生态系统统计分析，考察事实核查组织的政治倾向，并尝试基于统计分析结果和政治倾向为每个组织分配可信度评分。我们构建 FACTors 的方法具有通用性，可用于维护一个能够动态更新的实时数据集。
英文摘要：
Our fight against false information is spearheaded by fact-checkers. They investigate the veracity of claims and document their findings as fact-checking reports. With the rapid increase in the amount of false information circulating online, the use of automation in fact-checking processes aims to strengthen this ecosystem by enhancing scalability. Datasets containing fact-checked claims play a key role in developing such automated solutions. However, to the best of our knowledge, there is no fact-checking dataset at the ecosystem level, covering claims from a sufficiently long period of time and sourced from a wide range of actors reflecting the entire ecosystem that admittedly follows widely-accepted codes and principles of fact-checking. We present a new dataset FACTors, the first to fill this gap by presenting ecosystem-level data on fact-checking. It contains 118,112 claims from 117,993 fact-checking reports in English (co-)authored by 1,953 individuals and published during the period of 1995-2025 by 39 fact-checking organisations that are active signatories of the IFCN (International Fact-Checking Network) and/or EFCSN (European Fact-Checking Standards Network). It contains 7,327 overlapping claims investigated by multiple fact-checking organisations, corresponding to 2,977 unique claims. It allows to conduct new ecosystem-level studies of the fact-checkers (organisations and individuals). To demonstrate the usefulness of FACTors, we present three example applications, including a first-of-its-kind statistical analysis of the fact-checking ecosystem, examining the political inclinations of the fact-checking organisations, and attempting to assign a credibility score to each organisation based on the findings of the statistical analysis and political leanings. Our methods for constructing FACTors are generic and can be used to maintain a live dataset that can be updated dynamically.
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