摘要
arXiv:2507.17636v2 Announce Type: replace Abstract: Negative campaigning is a defining feature of electoral competition, yet comparative research on its drivers has remained limited by the high cost and limited scalability of existing classification methods. This study makes two key contributions. First, it evaluates zero-shot large language models (LLMs) as a scalable method for cross-lingual classification of negative campaigning. Using benchmark datasets in ten languages, we show that LLM classifications closely match native-speaker human annotations while outperforming conventional supervised models. Second, we leverage this approach to conduct, to our knowledge, the largest cross-national study of negative campaigning to date, analyzing 18 million tweets posted by parliamentarians in 19 European countries between 2017 and 2022.