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

2025-01-15

AI Summary

This study investigates the propagation mechanisms and ideological diffusion pathways of English-language disinformation across a heterogeneous web ecosystem. Addressing the limitation of existing methods in detecting latent propaganda networks, we propose a novel framework integrating zero-shot stance detection with NETINF-based network inference to trace cross-site dissemination, stance evolution, and temporal narrative shifts of 146K English news stories across 4,000+ credible and low-credibility websites. Our key contributions include: (i) the first stance-aware network inference method requiring no labeled training data; (ii) automatic identification of covert propaganda subnetworks (e.g., anti-vaccine, anti-Ukraine); and (iii) quantification of website influence hierarchies. Experiments reveal accelerating narrative polarization dynamics and the distribution of pivotal influence sources, yielding interpretable, actionable empirical insights for fact-checking and ecosystem-level interventions.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Fake News
• Information Dissemination
• Propaganda Networks

Innovation

• Advanced Language Processing
• Misinformation Tracking
• NETINF Algorithm