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

2025-09-27

AI Summary

This study investigates how political stance and cultural perspective influence large language models’ (LLMs) identification of offensive content in multilingual political tweets. Addressing the lack of ideological and cultural sensitivity in existing detection methods, we propose a personalized offensiveness assessment framework grounded in role-based prompting and chain-of-thought reasoning. We conduct systematic experiments across six mainstream LLMs—including DeepSeek-R1, Qwen3, and GPT-4.1-mini—on the MD-Agreement multilingual dataset. Results demonstrate that models with explicit reasoning capabilities achieve greater consistency and granularity in cross-lingual and cross-ideological settings; role-guided prompting significantly enhances modeling of cultural context and stance dependency. This work provides the first empirical evidence that reasoning mechanisms critically improve interpretability, judgment consistency, and personalized detection performance. It establishes a novel paradigm for value-aware NLP systems, advancing fairness and contextual fidelity in offensive language detection.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Argumentation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Assess offensiveness in political tweets from varied ideological perspectives
• Evaluate LLM sensitivity to cultural and political variations across languages
• Improve personalization of offensiveness detection using reasoning capabilities

Innovation

• LLMs adopt specific political cultural perspectives
• Reasoning abilities improve personalization interpretability offensiveness judgments
• Models adapt nuanced sociopolitical classification across languages ideologies