Measuring Negative Campaigning across Languages with Large Language Models: A Study of 18 Million Tweets in 19 Countries

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. Building on a strategic incentives framework, we argue that governing and coalition-oriented parties face stronger reputational constraints against negative campaigning, whereas opposition and outsider parties face weaker constraints. We further expect parties located away from the ideological center, especially on the radical right, to rely more heavily on confrontational rhetoric. The results support these expectations: cabinet parties are less negative, while ideologically non-centrist parties--most notably radical right parties--are substantially more negative. The study thus provides new comparative evidence on the party-level foundations of campaign negativity while demonstrating how LLMs can transform the scale, consistency, and replicability of research on political discourse across languages and institutional contexts.