Do LLMs Transfer Political Framing across Languages? A Cross-Lingual Analysis of LLM-Generated Discourse

Abstract

As large language models (LLMs) increasingly mediate political information across linguistic contexts, concerns emerge regarding cross-lingual consistency in political framing. We investigate whether multilingual LLMs generate systematically different rhetorical and semantic frames when prompted in English versus Arabic on the politically salient issue of migration. Focusing on two widely used models, i.e., GPT-4o and Jais-13B, we implement a controlled prompt design (N = 800 generations; 400 per language), to isolate language as the primary experimental variable. We introduce a mixed method evaluation framework that combines lexical frame analysis, statistical association testing, and qualitative discourse analysis. Our results show a significant association between language and framing distribution (χ2 = 43.32, p = 2.11 × 10^−9). While security-oriented framing is prominent in both languages, English generations exhibit substantially higher rates of institutional and legislative framing, whereas Arabic generations show greater concentration in security and communitarian discourse. These findings indicate that input language acts as a conditioning signal that systematically modulates political framing within multilingual LLMs, even under controlled semantic prompts. We conceptualize this phenomenon as cross-lingual framing drift and discuss its implications for multilingual alignment, political bias evaluation, and global information ecosystems. We conclude by outlining an evaluative protocol for detecting language-conditioned asymmetries in generative models. We make all data, code, and experimental settings publicly available at: https://github.com/NRAwwad/ -A-Cross-Lingual-Analysis-of-Political-Framing-in-English-and-Arabic.git.

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