论文标题
多元宇宙：虚假新闻检测的多语言证据
Multiverse: Multilingual Evidence for Fake News Detection
论文作者
论文摘要
误导信息以惊人的速度在互联网上传播，在某些情况下可能会导致无法弥补的后果。开发虚假新闻检测技术已经成为必不可少的。尽管已经朝这个方向完成了实质性的工作，但当前方法的局限性之一是这些模型仅专注于一种语言，并且不使用多语言信息。在这项工作中，我们提出了Multiverse，这是一项基于多语言证据的新功能，可用于假新闻检测并改善现有方法。首先，基于一组已知的真实和假新闻，通过手动实验证实了将跨语性证据作为假新闻检测特征的一种假设。之后，我们将基于拟议功能的虚假新闻分类系统与两个多域通用新闻数据集和一个伪造的COVID-19新闻数据集进行了比较，表明与语言功能的其他结合相结合，它会产生重大改进。
Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. It is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose Multiverse -- a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. After that, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset showing that in additional combination with linguistic features it yields significant improvements.
