Misinformation, propaganda, and outright lies proliferate on the web, with some narratives having dangerous real-world consequences on public health, elections, and individual safety. However, despite the impact of misinformation, the research community largely lacks automated and programmatic approaches for tracking news narratives across online platforms. In this work, utilizing daily scrapes of 1,404 unreliable news websites, the large-language model MPNet, and DP-Means clustering, we introduce a system to automatically isolate and analyze the narratives spread within online ecosystems. Identifying 55,301 narratives on these 1,404 websites, we describe the most prevalent narratives spread in 2022 and identify the most influential websites that originate and magnify narratives. Finally, we show how our system can be utilized to detect new narratives originating from unreliable news websites and aid fact-checkers like Politifact, Reuters, and AP News in more quickly addressing misinformation stories.

翻译：虚假信息、宣传和彻头彻尾的谎言在网络上泛滥，其中一些叙事对公共健康、选举和个人安全造成了危险的现实世界后果。然而，尽管虚假信息影响巨大，研究界在很大程度上缺乏自动化和程序化的方法来跨在线平台追踪新闻叙事。在这项工作中，利用对1,404个不可靠新闻网站的每日抓取、大型语言模型MPNet和DP-Means聚类，我们引入了一个系统来自动隔离和分析在线生态系统中传播的叙事。通过识别这1,404个网站上的55,301个叙事，我们描述了2022年传播最广泛的叙事，并确定了起源和放大叙事的最具影响力的网站。最后，我们展示了如何利用我们的系统检测源自不可靠新闻网站的新叙事，并帮助像Politifact、路透社和美联社这样的事实核查机构更快速地应对虚假信息故事。