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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.12907 |
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| _version_ | 1866916488669036544 |
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| author | Schulz, Lion Patrício, Miguel Odijk, Daan |
| author_facet | Schulz, Lion Patrício, Miguel Odijk, Daan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Narrative Information Theory Schulz, Lion Patrício, Miguel Odijk, Daan Multimedia Information Theory 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. |
| title | Narrative Information Theory |
| topic | Multimedia Information Theory |
| url | https://arxiv.org/abs/2411.12907 |