Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Kai-Robin Lange, Tobias Schmidt, Matthias Reccius, Henrik M\"uller, Michael Roos, Carsten Jentsch

TL;DR
This paper introduces a hybrid method combining dynamic topic models and large language models to detect and interpret narrative shifts over time in media, balancing computational efficiency with narrative understanding.
Contribution
It presents a novel pipeline that integrates topic modeling and LLMs to identify and analyze narrative changes in large corpora, addressing computational challenges.
Findings
LLMs can efficiently detect narrative shifts at specific points.
The method distinguishes between content and narrative shifts.
The approach is applied to WSJ articles from 2009-2023.
Abstract
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction methods such as Large Language Models do well in capturing typical narrative elements or even the complex structure of a narrative, applying them to an entire corpus comes with obstacles, such as a high financial or computational cost. We propose a combination of the language understanding capabilities of Large Language Models with the large scale applicability of topic models to dynamically model narrative shifts across time using the Narrative Policy Framework. We apply a topic model and a corresponding change point detection method to find changes that concern a specific topic of interest. Using this model, we filter our corpus for documents that are particularly…
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Taxonomy
TopicsTopic Modeling · Computational and Text Analysis Methods
