Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks
Erica Cai, Xi Chen, Reagan Grey Keeney, Ethan Zuckerman, Brendan O'Connor, Przemyslaw A. Grabowicz

TL;DR
This paper presents FAME, an AI-powered, scalable method for identifying news articles about critical global events across multiple languages using minimal metadata, enabling large-scale comparative coverage studies.
Contribution
Introduction of FAME, a training-free, metadata-based method that efficiently matches news articles to events across massive multilingual databases, achieving state-of-the-art performance.
Findings
Coverage correlates with death tolls and economic factors.
FAME scales to tens of millions of articles and hundreds of events.
Patterns align with prior literature on media coverage.
Abstract
Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedia Studies and Communication · Media Influence and Politics
