Nuclear energy has been a highly controversial issue in post-war (West) German politics. Previous research has identified discourse on nuclear energy as an important factor in the development of German political culture. However, little work has been done on the quantitative analysis of this discourse, despite the availability of large amounts of text data. We use large language models to classify texts in a corpus of Bundestag proceedings and news articles according to speakers’ stance and framing. In combination with the texts’ metadata, this enables us to draw conclusions about the positions taken by political parties and media publications on nuclear energy over time. We find that while media reporting remained mostly neutral, Bundestag speakers became divided on the issue along party lines by the 1970s. The framing of nuclear energy in both media and politics shifted in response to events and policy needs. More generally, our approach could be applied to other problems in discourse analysis where large amounts of data are available but no high-quality annotations for classifier training exist.
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Maximilian Teich
University of Passau
Arne Cypionka
University of Passau
Thomas Haider
University of Passau
Histories
University of Passau
Building similarity graph...
Analyzing shared references across papers
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Teich et al. (Wed,) studied this question.
synapsesocial.com/papers/6a192eb9fab5b468c4417ed5 — DOI: https://doi.org/10.3390/histories6020034