Mapping News Narratives Using LLMs and Narrative-Structured Text Embeddings

TL;DR

This paper introduces a novel computational method that uses large language models and narrative-structured embeddings based on linguistic theory to analyze and differentiate news narratives, demonstrated on conflict-related articles.

Contribution

It presents a new numerical narrative representation grounded in structuralist linguistic theory, enabling comprehensive and generalizable analysis of news narratives using LLMs.

Findings

Successfully distinguishes articles with similar topics but different narratives

Demonstrates applicability on 5000 news articles from major outlets

Captures both semantics and narrative structure in embeddings

Abstract

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.