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Embracing Dialectic Intersubjectivity: Coordination of Different Perspectives in Content Analysis with LLM Persona Simulation

arXiv 2502.00903 ↗First posted 2 Feb 2025cs.CL cs.AI cs.CYcited by 0

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Abstract (arXiv)

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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References · 14

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  2. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
  3. 2018Computational Optimal Transportcited by 765
  4. 2023Whose Opinions Do Language Models Reflect?cited by 759
  5. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  6. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  7. 2023S3: Social-network Simulation System with Large Language Model-Empowered Agentscited by 228
  8. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
  9. 2025The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMscited by 72
  10. 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
  11. 2023LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Codingcited by 64
  12. 2023AI Alignment and Social Choice: Fundamental Limitations and Policy Implicationscited by 35
2 more
  1. 2024Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targetscited by 31
  2. 2024ChatGPT v.s. Media Bias: A Comparative Study of GPT-3.5 and Fine-tuned Language Modelscited by 2

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