Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increasingly critical. In this study, we address the challenge of understanding political bias in digitized discourse using LLMs. While traditional approaches often rely on finetuning separate models for each political faction, our work innovates by employing a singular, instruction-tuned LLM to reflect a spectrum of political ideologies. We present a comprehensive analytical framework, consisting of Partisan Bias Divergence Assessment and Partisan Class Tendency Prediction, to evaluate the model's alignment with real-world political ideologies in terms of stances, emotions, and moral foundations. Our findings reveal the model's effectiveness in capturing emotional and moral nuances, albeit with some challenges in stance detection, highlighting the intricacies and potential for refinement in NLP tools for politically sensitive contexts. This research contributes significantly to the field by demonstrating the feasibility and importance of nuanced political understanding in LLMs, particularly for applications requiring acute awareness of political bias.

翻译：社交媒体平台上充斥着政治色彩浓厚的讨论。因此，利用大型语言模型（LLMs）准确解读和预测党派偏见日益重要。在本研究中，我们运用LLMs应对数字化话语中政治偏见理解的挑战。传统方法通常需要为每个政治派别分别微调模型，而本研究的创新之处在于采用单一指令调优的LLM来反映一系列政治意识形态。我们提出了一个全面的分析框架，包括党派偏见分歧评估和党派类别倾向预测，以评估模型在立场、情感和道德基础方面与现实世界政治意识形态的一致性。研究结果表明，该模型在捕捉情感和道德细微差异方面效果显著，但在立场检测方面存在一定挑战，这凸显了在政治敏感语境下自然语言处理工具的精妙之处和改进潜力。本研究通过展示LLMs具备细致政治理解能力的可行性及重要性，为该领域做出了重要贡献，尤其适用于需要高度政治偏见敏感度的应用场景。