  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 countries and 10
languages, detecting 4357 news events. We identify the factors explaining
diversity and synchrony of news coverage across countries. Our study reveals
that news media tend to cover a more diverse set of events in countries with
larger Internet penetration, more official languages, larger religious
diversity, higher economic inequality, and larger populations. Coverage of news
events is more synchronized between countries that not only actively
participate in commercial and political relations -- such as, pairs of
countries with high bilateral trade volume, and countries that belong to the
NATO military alliance or BRICS group of major emerging economies -- but also
countries that share certain traits: an official language, high GDP, and high
democracy indices.
