多语言、多模态流水线用于创建真实且结构化的事实核查数据集
Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Dataset
AI总结：
本文提出一个多语言、多模态的数据收集与处理流水线，通过整合ClaimReview feeds、抓取完整驳斥文章、标准化异构声明裁定并添加结构化元数据和对齐的视觉内容，构建了法语和德语的事实核查数据集，提升了事实核查的可解释性和证据基础。
摘要：
在线平台上虚假信息的快速传播凸显了建立稳健、及时、可解释且多语言的事实核查资源的紧迫性。然而，现有数据集范围有限，通常缺乏多模态证据、结构化注释和声明、证据和裁定之间的详细链接。本文介绍了一个全面的数据收集和处理流水线，通过聚合ClaimReview feeds、抓取完整驳斥文章、标准化异构声明裁定，并添加结构化元数据和对齐的视觉内容，构建了法语和德语的多模态事实核查数据集。我们使用最先进的大型语言模型（LLMs）和多模态LLMs进行（i）证据提取（在预定义的证据类别下）和（ii）证据与裁定之间的论证生成。通过G-Eval和人工评估证明，我们的流水线能够实现不同组织或媒体市场事实核查实践的细粒度比较，促进更可解释和证据基础的事实核查模型的发展，并为未来的多语言、多模态虚假信息验证研究奠定基础。
英文摘要：
The rapid proliferation of misinformation across online platforms underscores the urgent need for robust, up-to-date, explainable, and multilingual fact-checking resources. However, existing datasets are limited in scope, often lacking multimodal evidence, structured annotations, and detailed links between claims, evidence, and verdicts. This paper introduces a comprehensive data collection and processing pipeline that constructs multimodal fact-checking datasets in French and German languages by aggregating ClaimReview feeds, scraping full debunking articles, normalizing heterogeneous claim verdicts, and enriching them with structured metadata and aligned visual content. We used state-of-the-art large language models (LLMs) and multimodal LLMs for (i) evidence extraction under predefined evidence categories and (ii) justification generation that links evidence to verdicts. Evaluation with G-Eval and human assessment demonstrates that our pipeline enables fine-grained comparison of fact-checking practices across different organizations or media markets, facilitates the development of more interpretable and evidence-grounded fact-checking models, and lays the groundwork for future research on multilingual, multimodal misinformation verification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
