@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",
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.",
}
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%0 Conference Proceedings
%T Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
%A Kostikova, Aida
%A Beese, Dominik
%A Paassen, Benjamin
%A Pütz, Ole
%A Wiedemann, Gregor
%A Eger, Steffen
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kostikova-etal-2024-fine
%X 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.
%R 10.18653/v1/2024.emnlp-main.337
%U https://aclanthology.org/2024.emnlp-main.337
%U https://doi.org/10.18653/v1/2024.emnlp-main.337
%P 5884-5907
Markdown (Informal)
[Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates](https://aclanthology.org/2024.emnlp-main.337) (Kostikova et al., EMNLP 2024)
ACL