arXiv cs.CY· Sarah Sulollari, Vanja Karan, Benjamin Roth·· 12 小时前AI 评分18
用微调 BERT 预测政党归属，分析奥地利议会演讲特征
Analysing Austrian Parliamentary Speeches by Predicting Party Affiliations
AI 导读
研究提出基于微调 BERT 模型的方法，为奥地利国民议会每篇演讲输出各政党概率分布，并按演讲者和政党层级聚合。结果显示联合执政的政党演讲特征趋于相似，反对党则与执政党渐行渐远；议员转党后通常只在转党之后才调整演讲特征。团队公开了 1996 至 2018 年奥地利国民议会演讲政党归属预测数据集。
正文 · 原文
Abstract:This paper examines speech characteristics of the Austrian National Council members (CMs). We introduce analysis methodology based on a fine-tuned BERT-Model which assigns to every speech a probability distribution over Austrian parties. These are then aggregated at the speaker and party level. We perform an extensive evaluation to ensure the quality of the generated distributions prior to further analysis. Instead of considering mis-assignments of predicted parties as a performance issue, we leverage them to derive insights about the CMs and political developments. Our findings show that parties that govern together become more similar in their speech characteristics, while, analogously, parties that are in opposition move further away from governing parties. Moreover, CMs who switch party usually adapt their speech characteristics only after the switch occurs. Finally, we publicly release a data set containing party affiliation predictions for the speeches of the Austrian National Council from 1996 to 2018.
| Comments: | KONVENS 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; I.7.0; J.4 |
| Cite as: | arXiv:2610.32515 [cs.CL] |
| (or arXiv:2610.32515v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32515 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sarah Sulollari [view email]
[v1]
Fri, 23 Oct 2026 11:38:35 UTC (676 KB)
来源：arXiv cs.CY · arxiv.org