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Transboundary News & Solutions Discourses: A Cross-National Topic Model Analysis of Climate Change Media Coverage

Authors: Lucy McAllister*, Babson College, Siddharth Vedula, Babson College, Maxwell Boykoff, University of Colorado Boulder
Topics: Environment, Environmental Perception, Quantitative Methods
Keywords: climate change; media; solutions; topic modeling
Session Type: Paper
Presentation File: No File Uploaded


Human-induced climate change threatens biodiversity, land erosion, and the very subsistence of humans on the planet (IPCC, 2018). In recent decades, there has been increasing public awareness and heightened concern about climate change (Howe et al., 2015), with the media playing a critical role in bridging the gap between scientific knowledge and societal response (Moser, 2016). In addition, the media has a significant influence on the ways in which climate change is understood, framed, and represented to the general public (Boykoff 2011; Hulme, 2009). In this study, we conduct a cross-national, longitudinal analysis of media reporting on climate change using data from 113 sources in 57 countries in 7 languages, over a 20-year period from 2000-2019. We implement a big-data approach, leveraging a computational text analysis methodology known as topic modeling (Bohr and Dunlap, 2018). Our method allows us to identify cross-national differences in the topics that are covered by print, radio, and television sources (e.g. health, renewable energy, water, development, policy, financial mechanisms) (O’Neill et al., 2015), as well as identify how the incidence of these topics has changed geographically over time. We build on prior research that has examined cross-national differences in climate communication, but has frequently focused either on a) aggregate counts of media coverage, or b) studying specific topics such as climate skepticism (Tranter and Booth, 2015), and public opinion (McCright et al., 2016). These findings can also further conversations on what constitutes effective transboundary climate change solutions discourses.

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