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