投票手册偏见：瑞士联邦传播中的立场检测
Voting Booklet Bias: Stance Detection in Swiss Federal Communication
AI总结：
本研究针对瑞士联邦投票手册是否中立呈现议题的问题，基于瑞士政治数据集微调M-BERT模型实现最优立场检测，分析2022年9月公投手册发现部分议题有偏向且跨语言结果一致，为投票手册编辑和政治话语分析系统设计提供参考。
摘要：
本研究采用最新的立场检测方法，研究面向选民的官方信息手册中陈述的立场（支持、反对或中立）。我们的核心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？\n为此，我们首先在一个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，发现微调M-BERT模型可取得最优准确率。随后，我们使用最优模型分析了从2022年9月瑞士全民公投相关的联邦投票手册中提取的表述的立场，这也是本项目的核心目标。\n我们在德语、法语和意大利语的多语言及单语言语境下均对模型进行了评估。分析显示，部分议题存在明显的立场偏向，而其他议题则更为均衡，且结果在不同语言间大体一致。\n我们的研究结果对未来投票手册的编辑流程以及更优质的政治话语自动化分析系统的设计具有参考意义。本文配套的数据和代码可在https://github.com/ZurichNLP/voting-booklet-bias获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
