Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover on only one or two languages, focus only on one task, or rely on external news article sets for sourcing true claims. To address these shortcomings, we introduce M4FC, a new real-world dataset comprising 4,982 images paired with 6,980 claims. The images, verified by professional fact-checkers from 22 organizations, represent a diverse range of cultural and geographic contexts. Each claim is available in one or two out of ten languages. M4FC spans six multimodal fact-checking tasks: visual claim extraction, claimant intent prediction, fake image detection, image contextualization, location verification, and verdict prediction. We provide baseline results for all tasks and analyze how combining intermediate tasks affects verdict prediction performance. We make our dataset and code publicly available.

翻译：现有面向多模态事实核查的真实世界数据集存在多重局限性：实例数量稀少，仅覆盖一两种语言，专注于单一任务，或依赖外部新闻文章集作为真实主张的来源。为解决上述不足，本文提出M4FC——一个包含4,982张图像及对应6,980条主张的新型真实世界数据集。这些图像经来自22个组织的专业事实核查人员验证，呈现了多元的文化与地理背景。每条主张以十种语言中的一种或两种呈现。M4FC涵盖六项多模态事实核查任务：视觉主张提取、主张者意图预测、虚假图像检测、图像情境化、位置验证及裁决预测。我们为所有任务提供了基线结果，并分析了中间任务组合对裁决预测性能的影响。数据集与代码已公开提供。