This paper analyses two hitherto unstudied sites sharing state-backed
disinformation, Reliable Recent News (rrn.world) and WarOnFakes
(waronfakes.com), which publish content in Arabic, Chinese, English, French,
German, and Spanish. We describe our content acquisition methodology and
perform cross-site unsupervised topic clustering on the resulting multilingual
dataset. We also perform linguistic and temporal analysis of the web page
translations and topics over time, and investigate articles with false
publication dates. We make publicly available this new dataset of 14,053
articles, annotated with each language version, and additional metadata such as
links and images. The main contribution of this paper for the NLP community is
in the novel dataset which enables studies of disinformation networks, and the
training of NLP tools for disinformation detection.

本研究分析了两个以国家支持为背景的尚未被研究的网站，Reliable Recent News (rrn.world) 和 WarOnFakes (waronfakes.com)，它们使用阿拉伯语、中文、英语、法语、德语和西班牙语发布内容。我们描述了获取内容的方法，并对多语言数据集进行了跨站点无监督主题聚类分析。我们还对网页翻译和主题进行了语言和时间分析，并调查了具有虚假发布日期的文章。我们开放了这个包含 14,053 篇文章的新数据集，每篇文章都标注了语言版本和其他元数据，如链接和图片。本文对自然语言处理社区的主要贡献在于提供了这一新颖数据集，使得可以研究虚假信息网络，并进行虚假信息检测工具的训练。