FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War

Gautam Kishore Shahi, Amit Kumar Jaiswal, Thomas Mandl
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Abstract

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87\%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion. The dataset is available on Github\footnote{https://github.com/Gautamshahi/FakeClaim}
FakeClaim:用于识别 2023 年以色列-哈马斯战争假新闻的多平台驱动数据集
我们提供了第一个公开可用的数据集,其中包含来自不同平台的事实陈述和关于 2023 年以色列-哈马斯战争的虚假 YouTube 视频,用于对虚假 YouTube 视频进行自动分类。FakeClaim 数据来自 60 个事实核查组织,使用 30 种语言,并由经过培训的事实核查专业记者从事实核查组织中收集元数据加以充实。此外,我们还利用文本信息和用户评论对 YouTube 视频子集中的虚假视频进行分类。我们使用预先训练好的模型,以不同的特征组合对每个视频进行分类。我们表现最好的微调语言模型--通用句子编码器(USE)--达到了 87% 的 Macro F1,这表明训练有素的模型有助于利用用户讨论中的评论来揭穿虚假视频。该数据集可在 Github\footnote{https://github.com/Gautamshahi/FakeClaim} 上获取。
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