用结构主义理论量化新闻叙事，区分同主题不同叙事的文章。
Mapping News Narratives Using LLMs and Narrative-Structured Text Embeddings
- 基于格雷马斯角色模型提取六类叙事角色，构建结构化文本嵌入。
- 在5000篇中东冲突新闻中验证，能准确区分相同话题下的不同叙事。
- 适合研究媒体偏见、舆论演化和跨文化叙事差异的学者与从业者。
叙事在个人身份到国际政治等社会层面具有深远影响，尤其在互联网空间中，其快速传播易加剧社会分裂。尽管已有诸多定性方法，但叙事的量化分析仍面临挑战，现有计算框架缺乏全面性和通用性。为此，本文提出一种基于结构主义语言学理论的数值化叙事表示方法，核心为格雷马斯的行动者模型（Actantial Model），该模型通过六种功能角色刻画叙事。这些角色不依赖具体语境，具备高度泛化能力。我们利用开源大模型提取这些角色，并将其整合进叙事结构化文本嵌入（Narrative-Structured Text Embedding），同时捕捉语义与叙事结构。在阿联酋半岛电视台与华盛顿邮报共5000篇关于以巴冲突的新闻文章上验证了该方法的有效性，结果表明其能有效区分主题相同但叙事结构不同的文章。
原文摘要 · Abstract (English)
Given the profound impact of narratives across various societal levels, from personal identities to international politics, it is crucial to understand their distribution and development over time. This is particularly important in online spaces. On the Web, narratives can spread rapidly and intensify societal divides and conflicts. While many qualitative approaches exist, quantifying narratives remains a significant challenge. Computational narrative analysis lacks frameworks that are both comprehensive and generalizable. To address this gap, we introduce a numerical narrative representation grounded in structuralist linguistic theory. Chiefly, Greimas' Actantial Model represents a narrative through a constellation of six functional character roles. These so-called actants are genre-agnostic, making the model highly generalizable. We extract the actants using an open-source LLM and integrate them into a Narrative-Structured Text Embedding that captures both the semantics and narrative structure of a text. We demonstrate the analytical insights of the method on the example of 5000 full-text news articles from Al Jazeera and The Washington Post on the Israel-Palestine conflict. Our method successfully distinguishes articles that cover the same topics but differ in narrative structure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。