«Yes, I understood that in German: wishy-washy!» Political polarization and the foreign language effect

Aims and objectives: When bilinguals use their weaker language, they tend to apply different rationalities and have less immediate emotional reactions (the so-called foreign-language effect ). The aim of this study is to test whether bilingual politicians differ between statements they make in their first (L1) or in their second language (L2). More specifically, we hypothesize that using L2 might be associated with less polarizing political language. Method: We analyze a corpus of more than 2,000 interactional turns uttered by 10 Swiss politicians in national TV debates in French and in German. All politicians appear both in French and in German, but one of the two languages is their L1, respectively. The politicians’ statements are assessed regarding their polarizing potential by a large language model (LLM) and by human raters. Data and analysis: Descriptive and inferential statistical analyses are then carried out to unveil individual and collective patterns of difference across L1 and L2 in the data. Findings: The results suggest a robust foreign effect in the analyzed data. It appears that engaging in political debates in non-dominant languages coincides with more factual and less emotional and less polarizing language. Originality: This study is the first one to investigate the foreign effect in politics, the first one to analyze not lab-elicited but natural conversational data, and the first one to present an operationalization of the classification of political polarizing speech for the use with LLMs. Implications: The study suggests that a foreign language effect is not confined to lab settings but extends to socially relevant contexts such as authentic political debates. There is a moderate positive correspondence between human and LLM ratings, which implies that LLMs may cautiously be used to classify large corpora of texts. Limitations: Further research is necessary to test and increase the validity and reliability of LLM-based text classifications.