Narrative Information Theory
Lion Schulz, Miguel Patr\'icio, Daan Odijk
TL;DR
This paper introduces an information-theoretic framework for analyzing narratives, enabling quantification of story complexity and emotional dynamics, with applications in media and AI storytelling.
Contribution
It presents a novel formalism to measure and benchmark narratives, bridging creative analysis and AI research.
Findings
Quantifies narrative complexity across genres
Analyzes emotional dynamics in stories
Provides tools for benchmarking AI-generated stories
Abstract
We propose an information-theoretic framework to measure narratives, providing a formalism to understand pivotal moments, cliffhangers, and plot twists. This approach offers creatives and AI researchers tools to analyse and benchmark human- and AI-created stories. We illustrate our method in TV shows, showing its ability to quantify narrative complexity and emotional dynamics across genres. We discuss applications in media and in human-in-the-loop generative AI storytelling.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling