A City of Millions: Mapping Literary Social Networks At Scale
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910898795315200 |
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| author | Hamilton, Sil Hicke, Rebecca M. M. Mimno, David Wilkens, Matthew |
| author_facet | Hamilton, Sil Hicke, Rebecca M. M. Mimno, David Wilkens, Matthew |
| contents | We release 70,509 high-quality social networks extracted from multilingual fiction and nonfiction narratives. We additionally provide metadata for $\sim$30,000 of these texts (73\% nonfiction and 27\% fiction) written between 1800 and 1999 in 58 languages. This dataset provides information on historical social worlds at an unprecedented scale, including data for 2,510,021 individuals in 2,805,482 pair-wise relationships annotated for affinity and relationship type. We achieve this scale by automating previously manual methods of extracting social networks; specifically, we adapt an existing annotation task as a language model prompt, ensuring consistency at scale with the use of structured output. This dataset serves as a unique resource for humanities and social science research by providing data on cognitive models of social realities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A City of Millions: Mapping Literary Social Networks At Scale Hamilton, Sil Hicke, Rebecca M. M. Mimno, David Wilkens, Matthew Computation and Language Machine Learning Social and Information Networks We release 70,509 high-quality social networks extracted from multilingual fiction and nonfiction narratives. We additionally provide metadata for $\sim$30,000 of these texts (73\% nonfiction and 27\% fiction) written between 1800 and 1999 in 58 languages. This dataset provides information on historical social worlds at an unprecedented scale, including data for 2,510,021 individuals in 2,805,482 pair-wise relationships annotated for affinity and relationship type. We achieve this scale by automating previously manual methods of extracting social networks; specifically, we adapt an existing annotation task as a language model prompt, ensuring consistency at scale with the use of structured output. This dataset serves as a unique resource for humanities and social science research by providing data on cognitive models of social realities. |
| title | A City of Millions: Mapping Literary Social Networks At Scale |
| topic | Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2502.19590 |