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Social media platforms are rife with politically charged discussions.Therefore, accurately deciphering and predicting partisan biases using LargeLanguage Models (LLMs) is increasingly critical. In this study, we address thechallenge of understanding political bias in digitized discourse using LLMs.While traditional approaches often rely on finetuning separate models for eachpolitical faction, our work innovates by employing a singular,instruction-tuned LLM to reflect a spectrum of political ideologies. We presenta comprehensive analytical framework, consisting of Partisan Bias DivergenceAssessment and Partisan Class Tendency Prediction, to evaluate the model'salignment with real-world political ideologies in terms of stances, emotions,and moral foundations. Our findings reveal the model's effectiveness incapturing emotional and moral nuances, albeit with some challenges in stancedetection, highlighting the intricacies and potential for refinement in NLPtools for politically sensitive contexts. This research contributessignificantly to the field by demonstrating the feasibility and importance ofnuanced political understanding in LLMs, particularly for applicationsrequiring acute awareness of political bias.

Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies