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Misinformation, propaganda, and outright lies proliferate on the web, withsome narratives having dangerous real-world consequences on public health,elections, and individual safety. However, despite the impact ofmisinformation, the research community largely lacks automated and programmaticapproaches for tracking news narratives across online platforms. In this work,utilizing daily scrapes of 1,334 unreliable news websites, the large-languagemodel MPNet, and DP-Means clustering, we introduce a system to automaticallyidentify and track the narratives spread within online ecosystems. Identifying52,036 narratives on these 1,334 websites, we describe the most prevalentnarratives spread in 2022 and identify the most influential websites thatoriginate and amplify narratives. Finally, we show how our system can beutilized to detect new narratives originating from unreliable news websites andto aid fact-checkers in more quickly addressing misinformation. We release codeand data at https://github.com/hanshanley/specious-sites.

Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale