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Examining political polarization in the German bundestag using large language models: historical trends and a contemporary analysis - large language model preparation
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Analyzing political polarization has become increasingly relevant, particularly in light of the recent government crisis in Germany. This research investigates how political polarization in Germany has evolved over time and identifies factors influencing polarization in the current electoral term (2021-2025). We utilize an ensemble of three Large Language Models, BERT, GPT-4o-mini, and LLaMA, to classify speeches in the German Bundestag as polarizing. This approach is complemented by sentiment and structural analysis. Our results show a significant increase in political polarization across the last two electoral terms, with the entry of the right-wing party Alternative für Deutschland (AfD) into the Bundestag occurring concurrently. Political parties, followed by topics discussed, have emerged as the most influential factors in polarization. Meanwhile, the recent dissolution of the governing coalition was only subtly indicated by a reduction of applause among governing parties.
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Large language model Model LLM Natural language processing NLP GPT 4o BERT LLaMA Ensemble models Political polarization Sentiment analysis Corpus Bundestag German parliament Politics Debates