A City of Millions: Mapping Literary Social Networks At Scale

TL;DR

This paper introduces a large-scale dataset of social networks extracted from a vast collection of multilingual texts, enabling new research into historical social worlds and relationships at an unprecedented scale.

Contribution

The authors automate the extraction of social networks from texts using language models, creating a comprehensive dataset of over 70,000 networks with detailed metadata and relationship annotations.

Findings

Dataset includes 2.5 million individuals and 2.8 million relationships.

Automated extraction ensures consistency and scalability.

Provides a valuable resource for humanities and social sciences.

Abstract

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.