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Conflict and Uncertainty as Drivers of Politicization of Socio-Scientific Issues: Media Dynamics During the COVID-19 Pandemic

Media
Knowledge
Communication
Big Data
Berend Barkela
University of Kaiserslautern-Landau
Berend Barkela
University of Kaiserslautern-Landau
Aidar Zinnatullin
University of Kaiserslautern-Landau
Michaela Maier
Universität Koblenz-Landau

Abstract

This presentation examines the media environment in which policymakers navigate when dealing with contested socio-scientific issues. For such complex issues, where scientific evidence is crucial for informed decision-making, science communication intersects with political communication (Scheufele, 2014). This leads to the politicization of scientific evidence in public debate which can be defined on three dimensions (Neumann et al., 2024): (1) emphasizing contradictions and conflicts, (2) framing news within existing partisan conflicts and political competition, and (3) exploiting the uncertainty of scientific evidence in political communication. The study to be presented investigates the processes and drivers behind the politicization of socio-scientific issues, using the COVID-19 pandemic as a case study. This pandemic serves as an ideal example of public debates shaped by emerging, conflicting, and uncertain evidence (Dunwoody, 2020). The key questions are: (a) to what extent do debates focus on public health versus power struggles, and (b) which type of media drove the coverage of the pandemic in a politicized manner? These questions are addressed by automated text analysis of articles about the pandemic in eleven major German news sources investigating the three key dimensions of politicization in news coverage: To measure conflict (1) and partisanship (2) in the news coverage, we first identified parties and politicians by utilizing Name Entity Recognition, which was followed by the “a la carte” word embeddings regression framework (Rodriguez, Spirling & Stewart, 2023) to show how a vector of target words (the names of politicians / political parties) appears in vector space in proximity to language connoting either conflict or support of measures tackling the COVID-19 crisis. The degrees of scientific uncertainty (3) in the news were measured using the latent semantic scaling (Watanabe, 2020). Derived estimates of three dimensions of politicization were tested to measure their degree of association with the media’s ideological leaning and the type of broadcasting (public or commercial). Preliminary findings reveal a significant degree of politicization in pandemic coverage, with substantial variation across time. For instance, conflicting frames often increase close to the enactment of legislation. The presentation will discuss the results for all three dimensions of politicization. The findings contribute a deeper understanding of how media coverage sets the parameters for political narratives and related public debate about contested socio-scientific issues.