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Tracking Topics and Frames Regarding Sustainability Transformations during the Onset of the COVID-19 Crisis

doi: 10.3390/su131911095
Many researchers and politicians believe that the COVID-19 crisis may have opened a “window of opportunity” to spur sustainability transformations. Still, evidence for such a dynamic is currently lacking. Here, we propose the linkage of “big data” and “thick data” methods for monitoring debates on transformation processes by following the COVID-19 discourse on ecological sustainability in Germany. We analysed variations in the topics discussed by applying text mining techniques to a corpus with 84,500 newspaper articles published during the first COVID-19 wave. This allowed us to attain a unique and previously inaccessible “bird’s eye view” of how these topics evolved. To deepen our understanding of prominent frames, a qualitative content analysis was undertaken. Furthermore, we investigated public awareness by analysing online search behaviour. The findings show an underrepresentation of sustainability topics in the German news during the early stages of the crisis. Similarly, public awareness regarding climate change was found to be reduced. Nevertheless, by examining the newspaper data in detail, we found that the pandemic is often seen as a chance for sustainability transformations—but not without a set of challenges. Our mixed-methods approach enabled us to bridge knowledge gaps between qualitative and quantitative research by “thickening” and providing context to data-driven analyses. By monitoring whether or not the current crisis is seen as a chance for sustainability transformations, we provide insights for environmental policy in times of crisis.
content analysis, Environmental effects of industries and plants, green deal, TJ807-830, SDG, Institut für Umweltwissenschaften und Geographie, TD194-195, NLP, Renewable energy sources, Environmental sciences, frames, GE1-350, natural language processing
content analysis, Environmental effects of industries and plants, green deal, TJ807-830, SDG, Institut für Umweltwissenschaften und Geographie, TD194-195, NLP, Renewable energy sources, Environmental sciences, frames, GE1-350, natural language processing
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
