This study examines the level of politicization and polarization
in COVID-19 news in U.S. newspapers and televised network news from
March to May 2020. Using multiple computer-assisted content
analytic approaches, we find that newspaper coverage is highly
politicized, network news coverage somewhat less so, and both
newspaper and network news coverage are highly polarized. We find
that politicians appear in newspaper coverage more frequently than
scientists, whereas politicians and scientists are more equally
featured in network news. The high degree of politicization and
polarization in initial COVID-19 coverage may have contributed to
polarization in U.S. COVID-19 attitudes.In late 2019, a novel
coronavirus, COVID-19, began to spread throughout the world.
COVID-19 was declared a public health emergency of international
concern by the World Health Organization on January 30 and a
pandemic on March 11, 2020 (World Health Organization, 2020). The
infection rate and death toll have been substantial; by the end of
May 2020, at least 6 million people had been infected and at least
369,000 had died globally. Many of the infections have been
concentrated in the United States, with at least 1.8 million
individuals infected and 100,000 individuals killed by COVID-19 in
the United States by the end of May .While COVID-19 poses a
significant risk, political responses and public perceptions in the
United States have been divided across political ideological
lines.This raises questions about the role that both politicians
and the media have played in amplifying politicization and
polarization of COVID-19, as this kind of news coverage can
influence public attitudes in ways that exacerbate partisan
divides.Examining the first months of COVID-19 news coverage may
therefore help us to better understand what informed the public’s
initial perceptions of COVID-19. Though research to date has not
examined politicization and polarization in COVID-19 news coverage,
recent research.investigating politicization (the degree that
politicians are mentioned in conjunction with the issue) and
polarization (how discussion varies based on the presence of actors
from different political parties) in climate change news coverage
offers a useful methodological approach for analyzing these
features in news content. We draw on this approach in the present
study, which uses both dictionary and unsupervised machine learning
methods to investigate the degree to which newspaper and network
news coverage of COVID-19 was polarized and politicized during the
first 3 months of heightened news coverage
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