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      Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites

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          Abstract

          Understanding how misleading and outright false information enters news ecosystems remains a difficult challenge that requires tracking how narratives spread across thousands of fringe and mainstream news websites. To do this, we introduce a system that utilizes encoder-based large language models and zero-shot stance detection to scalably identify and track news narratives and their attitudes across over 4,000 factually unreliable, mixed-reliability, and factually reliable English-language news websites. Running our system over an 18 month period, we track the spread of 146K news stories. Using network-based interference via the NETINF algorithm, we show that the paths of news narratives and the stances of websites toward particular entities can be used to uncover slanted propaganda networks (e.g., anti-vaccine and anti-Ukraine) and to identify the most influential websites in spreading these attitudes in the broader news ecosystem. We hope that increased visibility into our distributed news ecosystem can help with the reporting and fact-checking of propaganda and disinformation.

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          Author and article information

          Journal
          15 January 2025
          Article
          2501.09102
          73b1ed02-e316-4c9f-99b8-6d0dee8fdeca

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          To appear at USENIX Security Symposium 2025. Keywords: Misinformation, News, Narratives, LLMs, Stance-Detection
          cs.SI cs.AI cs.CY cs.LG

          Social & Information networks,Applied computer science,Artificial intelligence

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