In this study, we use recent stance detection methods to study the stance (for, against or neutral) of statements in official information booklets for voters. Our main goal is to answer the fundamental question: are topics to be voted on presented in a neutral way? To this end, we first train and compare several models for stance detection on a large dataset about Swiss politics. We find that fine-tuning an M-BERT model leads to the best accuracy. We then use our best model to analyze the stance of utterances extracted from the Swiss federal voting booklet concerning the Swiss popular votes of September 2022, which is the main goal of this project. We evaluated the models in both a multilingual as well as a monolingual context for German, French, and Italian. Our analysis shows that some issues are heavily favored while others are more balanced, and that the results are largely consistent across languages. Our findings have implications for the editorial process of future voting booklets and the design of better automated systems for analyzing political discourse. The data and code accompanying this paper are available at https://github.com/ZurichNLP/voting-booklet-bias.

翻译：在本研究中，我们利用最新的立场检测方法，分析面向选民的官方信息手册中表述的立场（支持、反对或中立）。主要目标是回答一个根本性问题：待投票议题是否以中立方式呈现？为此，我们首先在关于瑞士政治的大型数据集上训练并比较了多种立场检测模型，发现对M-BERT模型进行微调可获得最佳准确率。随后，我们使用最优模型分析从瑞士联邦投票手册（涉及2022年9月瑞士全民公投）中提取的表述立场——这是本项目的核心目标。我们在多语言及单语（德语、法语、意大利语）情境下评估了模型性能。分析表明，某些议题受到明显偏向，而其他议题则更为均衡，且结果在不同语言间高度一致。本研究结论对未来投票手册的编辑流程以及更优政治话语自动化分析系统的设计具有启示意义。本文配套数据与代码可从https://github.com/ZurichNLP/voting-booklet-bias获取。