Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites
This addresses the problem of disinformation spread for journalists and fact-checkers, though it is incremental as it builds on existing methods like NETINF.
The researchers tackled the challenge of tracking how misleading information spreads across news ecosystems by developing a system that uses large language models and stance detection to monitor over 4,000 websites, identifying 146K stories and uncovering propaganda networks like anti-vaccine and anti-Ukraine.
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.