International political bias in large language models: a critical discourse analysis of narratives in ChatGPT, LLaMA, Gemini, and DeepSeek

This article explores the issue of international political bias in large language models (LLMs), using ChatGPT, Llama, Gemini, and DeepSeek as case studies. The research examines how these models construct assessments of key geopolitical actors–namely, the United States, Russia, China, Iran, and Israel– through the combined methodological lens of critical discourse analysis (CDA) and the emotional turn in International Relations (IR) theory. The study employs both automated sentiment analysis (via the NRC Emotion Lexicon) and qualitative discourse analysis of LLM-generated texts. The findings reveal a persistent structural asymmetry in the representation of states, which correlates with dominant narratives of Western political elites: the United States and China are predominantly associated with positive emotions (trust, joy), whereas Russia and Iran are more frequently linked to negative emotional frames (anger, fear). Although Israel’s foreign policy is subject to some criticism, its portrayal remains considerably more neutral compared to that of Russia and Iran. Moreover, LLMs appear to adopt discursive strategies of soft power, promoting Western normative concepts such as human rights and a “rules-based international order,” while marginalizing alternative geopolitical perspectives. Notably, the analysis also uncovers instances of criticism directed at U.S. foreign policy, suggesting that such bias is not uniform and may vary across contexts. The study contributes to ongoing scholarly debates on the role of AI in public diplomacy, algorithmic power, and technological sovereignty, underscoring the need for regulatory mechanisms to mitigate ideological distortions embedded in the outputs of generative language models.