Dominik Beese
2024
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Aida Kostikova
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Dominik Beese
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Benjamin Paassen
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Ole Pütz
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Gregor Wiedemann
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Steffen Eger
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Solidarity is a crucial concept to understand social relations in societies. In this study, we investigate the frequency of (anti-)solidarity towards women and migrants in German parliamentary debates between 1867 and 2022. Using 2,864 manually annotated text snippets, we evaluate large language models (LLMs) like Llama 3, GPT-3.5, and GPT-4. We find that GPT-4 outperforms other models, approaching human annotation accuracy. Using GPT-4, we automatically annotate 18,300 further instances and find that solidarity with migrants outweighs anti-solidarity but that frequencies and solidarity types shift over time. Most importantly, group-based notions of (anti-)solidarity fade in favor of compassionate solidarity, focusing on the vulnerability of migrant groups, and exchange-based anti-solidarity, focusing on the lack of (economic) contribution. This study highlights the interplay of historical events, socio-economic needs, and political ideologies in shaping migration discourse and social cohesion.
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