用大模型模拟不同立场，分析新闻内容中的偏见与共识动态。
Embracing Dialectic Intersubjectivity: Coordination of Different Perspectives in Content Analysis with LLM Persona Simulation
- 用六种GPT-4o配置模拟左右派立场，对比分析政治言论。
- 同立场模型间编码一致性更高，政治认同内容偏差更明显。
- 适合研究媒体偏见、社会科学研究中的人工智能协作机制。
本研究将内容分析方法从追求共识转向协调多元视角，探索不同观点间的动态关系。作为初步尝试，我们评估了六种GPT-4o配置在2020年美国总统大选期间对福克斯新闻和微软全国广播公司（MSNBC）关于拜登与特朗普的新闻稿进行情感分析的表现，考察模型间模式差异。通过评估各模型与意识形态立场的契合度，探讨在大语言模型辅助内容分析（LACA）中如何识别党派选择性处理现象。结果显示，具有党派身份的大模型在处理与其立场一致的内容时表现出更强的意识形态偏差；同一党派模型之间的编码者间可靠性高于跨党派组合。该方法提升了对大模型输出的细致理解，增强了人工智能驱动社会科学的完整性，可模拟现实世界影响。
原文摘要 · Abstract (English)
This study attempts to advancing content analysis methodology from consensus-oriented to coordination-oriented practices, thereby embracing diverse coding outputs and exploring the dynamics among differential perspectives. As an exploratory investigation of this approach, we evaluate six GPT-4o configurations to analyze sentiment in Fox News and MSNBC transcripts on Biden and Trump during the 2020 U.S. presidential campaign, examining patterns across these models. By assessing each model's alignment with ideological perspectives, we explore how partisan selective processing could be identified in LLM-Assisted Content Analysis (LACA). Findings reveal that partisan persona LLMs exhibit stronger ideological biases when processing politically congruent content. Additionally, intercoder reliability is higher among same-partisan personas compared to cross-partisan pairs. This approach enhances the nuanced understanding of LLM outputs and advances the integrity of AI-driven social science research, enabling simulations of real-world implications.
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