Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Aida Kostikova, Dominik Beese, Benjamin Paassen, Ole Pütz, Gregor Wiedemann, Steffen Eger
Abstract
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.
BibTeX
@inproceedings{kostikova-etal-2024-fine,
title = "Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of {G}erman Parliamentary Debates",
author = {Kostikova, Aida and
Beese, Dominik and
Paassen, Benjamin and
P{\"u}tz, Ole and
Wiedemann, Gregor and
Eger, Steffen},
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.337/",
doi = "10.18653/v1/2024.emnlp-main.337",
pages = "5884--5907"
}