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

TL;DR

This paper presents a scalable system using large language models and network analysis to track and analyze the spread of news narratives and attitudes across thousands of news websites, including unreliable sources.

Contribution

It introduces a novel system combining encoder-based language models and zero-shot stance detection for large-scale narrative tracking across diverse news sites.

Findings

Tracked 146,000 news stories over 18 months.

Identified propaganda networks and influential websites.

Revealed pathways of misinformation spread.

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.