Data and code for the paper "Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates" by Aida Kostikova, Dominik Beese, Benjamin Paassen, Ole Pütz, Gregor Wiedemann, and Steffen Eger, EMNLP 2024.

The repository contains the following elements:

• 📂 Data
  • 📂 Datasets: sentences containing a woman or migrant keyword
  • 📂 HumanAnnotatedDataset: human-annotated benchmark data
  • 📂 ModelPredictedData: model-predicted labels
• 📂 ExperimentsScripts: scripts used for training, fine-tuning, and inference experiments
• 📂 Analysis: code used to generate the plots
• 📂 archive/arxiv_v1: archived materials corresponding to the earlier arXiv v1 version

See DominikBeese/DeuParl-v2 for the full dataset of plenary protocols from the German Reichstag and Bundestag.

Unless otherwise noted, the code and documentation in this repository are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Materials in Data/HumanAnnotatedDataset/ are subject to separate terms described in that folder’s README.md and LICENSE files.

@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},
	year = 2024,
	month = 11,
	booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
	publisher = {Association for Computational Linguistics},
	address = {Miami, Florida, USA},
	pages = {5884--5907},
	doi = {10.18653/v1/2024.emnlp-main.337},
	url = {https://aclanthology.org/2024.emnlp-main.337/},
	editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
}

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.