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 1,334 unreliable news websites, the large-language model MPNet, and DP-Means clustering, we introduce a system to automatically identify and track the narratives spread within online ecosystems. Identifying 52,036 narratives on these 1,334 websites, we describe the most prevalent narratives spread in 2022 and identify the most influential websites that originate and amplify narratives. Finally, we show how our system can be utilized to detect new narratives originating from unreliable news websites and to aid fact-checkers in more quickly addressing misinformation. We release code and data at https://github.com/hanshanley/specious-sites.

翻译：错误信息、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卫生、选举和个人安全产生了危险的现实后果。然而，尽管错误信息影响深远，研究界在很大程度上缺乏自动化、程序化的方法，用于跨在线平台追踪新闻叙事。在这项工作中，利用对1,334个不可靠新闻网站的每日爬取、大型语言模型MPNet以及DP-Means聚类，我们引入了一个系统，用于自动识别和追踪在线生态系统中传播的叙事。通过在这些1,334个网站上识别出52,036个叙事，我们描述了2022年传播最广泛的叙事，并确定了发起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，并帮助事实核查人员更快地应对错误信息。我们在https://github.com/hanshanley/specious-sites上发布了代码和数据。