Global News Synchrony and Diversity During the Start of the COVID-19 Pandemic
Xi Chen, Scott A. Hale, David Jurgens, Mattia Samory, Ethan Zuckerman,, Przemyslaw A. Grabowicz

TL;DR
This paper introduces a scalable computational framework for analyzing global news coverage, revealing how factors like internet access, language diversity, and international relations influence news diversity and synchrony during the COVID-19 pandemic.
Contribution
The authors develop a novel, scalable methodology combining multilingual news similarity, event detection, and measures of news diversity and synchrony, enabling large-scale empirical analysis.
Findings
News coverage is more diverse in countries with higher internet penetration and linguistic diversity.
Countries with strong economic ties and shared traits exhibit higher news synchrony.
The methodology successfully analyzes 60 million articles across 124 countries, detecting 4357 global news events.
Abstract
News coverage profoundly affects how countries and individuals behave in international relations. Yet, we have little empirical evidence of how news coverage varies across countries. To enable studies of global news coverage, we develop an efficient computational methodology that comprises three components: (i) a transformer model to estimate multilingual news similarity; (ii) a global event identification system that clusters news based on a similarity network of news articles; and (iii) measures of news synchrony across countries and news diversity within a country, based on country-specific distributions of news coverage of the global events. Each component achieves state-of-the art performance, scaling seamlessly to massive datasets of millions of news articles. We apply the methodology to 60 million news articles published globally between January 1 and June 30, 2020, across 124…
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Taxonomy
TopicsMisinformation and Its Impacts
MethodsSparse Evolutionary Training
