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Understanding discourse and language of risk

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  • Jens O. Zinn
  • Marcus Müller

Abstract

Understanding risk communication in the public sphere is important for risk studies since the management of risk not only requires decision makers and experts to communicate risk well but also to understand how risk is debated in the public sphere more broadly. This special issue therefore discusses approaches which examine the linguistic representation of risk in text and how it changes over time. With the increasing body of digitised text available for research, scholars are now able to use advanced quantitative content analysis, text mining technologies, and corpus linguistic computational tools for the analysis of large text corpora. These advancements are also useful for risk studies and social science research. Specifically, the news media and other bodies of large text (corpora) built from, for example, parliamentary debate, social media, or government websites, have become a valuable resource for the analysis of language and discourse of risk which can be used to better understand the dynamics of risk communication in the public sphere. The contributions exemplify different research strategies by means of a number of case studies (e.g. terrorism, obesity, fracking, climate change, pre-exposure prophylaxis, migration as well as diachronic analysis of the use of risk in parliamentary debate and news coverage) and discuss key aspects of applying such research tools. This introduction outlines the central features of different ways to approach risk through discourse and language, before highlighting unresolved issues and the prospects for research and methodology of corpus-based risk studies and, finally, introducing the contributions.

Suggested Citation

  • Jens O. Zinn & Marcus Müller, 2022. "Understanding discourse and language of risk," Journal of Risk Research, Taylor & Francis Journals, vol. 25(3), pages 271-284, March.
  • Handle: RePEc:taf:jriskr:v:25:y:2022:i:3:p:271-284
    DOI: 10.1080/13669877.2021.2020883
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