2020
DOI: 10.1177/1075547019900290
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Politicization and Polarization in Climate Change News Content, 1985-2017

Abstract: Despite concerns about politicization and polarization in climate change news, previous work has not been able to offer evidence concerning long-term trends. Using computer-assisted content analyses of all climate change articles from major newspapers in the United States between 1985 and 2017, we find that media representations of climate change have become (a) increasingly politicized, whereby political actors are increasingly featured and scientific actors less so and (b) increasingly polarized, in that Dem… Show more

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Cited by 185 publications
(159 citation statements)
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“…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 by Chinn et al (2020) 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 (March, April, and May 2020).…”
mentioning
confidence: 99%
“…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 by Chinn et al (2020) 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 (March, April, and May 2020).…”
mentioning
confidence: 99%
“…Studies gauging the impact of media coverage on the public understanding of climate change (e.g. Feldman et al, 2015;Brevini and Lewis, 2018) have explored how political, corporate or consumerist discourses are contesting the weight of evidence about the causes and consequences of this phenomenon in the public arena; recent research has also revealed the extent to which collective perceptions of climate change reflect the considerable ground that political actors have gained vis-à-vis their scientific counterparts in climate news coverage over the last three decades (Chinn et al, 2020). As digital media outlets continue to increase the public's exposure to a widening range of competing climate change discourses animated by an ever more varied array of participants and stakeholders, the reasons why individuals "choose news outlets where they expect to find culturally congruent arguments about climate change" that are consistent with their "cultural way of life" (Newman et al, 2018, p. 985) are becoming an object of increasing research interest.…”
mentioning
confidence: 99%
“…Content analysis has been defined as a research methodology that objectively and systematically describes the content of a given body of communication [52]. It has been commonly used to analyze policy-related topics (e.g., [53,54]). Argument mining is a technique that identifies argumentation structures in a discourse [55].…”
Section: Control (Cont)mentioning
confidence: 99%