Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs

Abstract--We explore how large language models (LLMs) assess offensiveness in political discourse when prompted to adopt specific political and cultural perspectives. Using a multilingual subset of the MD-Agreement dataset centered on tweets from the 2020 US elections, we evaluate several recent LLMs - including DeepSeek-R1, o4-mini, GPT -4.1-mini, Qwen3, Gemma, and Mistral - tasked with judging tweets as offensive or non-offensive from the viewpoints of varied political personas (far-right, conservative, centrist, progressive) across English, Polish, and Russian contexts. Our results show that larger models with explicit reasoning abilities (e.g., DeepSeek-R1, o4-mini) are more consistent and sensitive to ideological and cultural variation, while smaller models often fail to capture subtle distinctions. We find that reasoning capabilities significantly improve both the personalization and interpretability of offensiveness judgments, suggesting that such mechanisms are key to adapting LLMs for nuanced sociopolitical text classification across languages and ideologies. Detecting offensive language is vital for fostering respectful discourse, particularly on social media. Y et, offensiveness is inherently subjective - shaped by individual ideologies, cultural backgrounds, and values [1], [2]. Supervised models rely on ground-truth labels that reflect annotators' biases, complicating efforts to build fair and robust systems [3]. Political discourse, marked by polarization, offers a compelling lens: individuals often tolerate combative language from their own side while labeling opposing views as offensive [4]. Recent advances in large language models (LLMs) - from GPT -based to instruction-tuned variants - have improved context-sensitive understanding [5]-[8].