用大模型解析生成式AI争议中的叙事结构，看清支持与反对背后的深层逻辑。
Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI
- 利用大模型提取文本的层级叙事框架，揭示论证背后的逻辑结构。
- 对比日本文化厅收集的公众观点，发现支持者与批评者关注的核心议题差异。
- 适合研究社会舆论、人工智能伦理或话语分析的研究者参考。
书面文本反映作者视角，深入分析文献是人文与社会科学的关键研究方法。然而，传统文本挖掘技术如情感分析和主题建模难以捕捉揭示深层论辩模式的层级叙事结构。为弥补这一不足，我们提出一种利用大语言模型（LLMs）提取并组织这些结构的方法，构建层级框架。通过分析日本文化厅收集的生成式AI公众意见，比较支持者与批评者的叙事差异，该方法清晰呈现了影响不同观点的关键因素，深化了对共识与分歧结构的理解。
原文摘要 · Abstract (English)
Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative AI collected by Japan's Agency for Cultural Affairs, comparing the narratives of supporters and critics. Our analysis provides clearer visualization of the factors influencing divergent opinions on generative AI, offering deeper insights into the structures of agreement and disagreement.
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