Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models
This work develops a knowledge-graph-augmented language-model pipeline for analyzing long, contested narratives. Using the first season of the Serial true-crime podcast as a case study, the system organizes people, claims, events, and relationships into a structure that can be queried while preserving connections across episodes and competing accounts.
Oct 1, 2024