The Social Lives of Literary Characters: Combining citizen science and language models to understand narrative social networks

Andrew Piper, Michael Xu, Derek Ruths


Abstract
Characters and their interactions are central to the fabric of narratives, playing a crucial role in developing readers’ social cognition. In this paper, we introduce a novel annotation framework that distinguishes between five types of character interactions, including bilateral and unilateral classifications. Leveraging the crowd-sourcing framework of citizen science, we collect a large dataset of manual annotations (N=13,395). Using this data, we explore how genre and audience factors influence social network structures in a sample of contemporary books. Our findings demonstrate that fictional narratives tend to favor more embodied interactions and exhibit denser and less modular social networks. Our work not only enhances the understanding of narrative social networks but also showcases the potential of integrating citizen science with NLP methodologies for large-scale narrative analysis.
Anthology ID:
2024.nlp4dh-1.45
Volume:
Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
Month:
November
Year:
2024
Address:
Miami, USA
Editors:
Mika Hämäläinen, Emily Öhman, So Miyagawa, Khalid Alnajjar, Yuri Bizzoni
Venue:
NLP4DH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
472–482
Language:
URL:
https://aclanthology.org/2024.nlp4dh-1.45
DOI:
10.18653/v1/2024.nlp4dh-1.45
Bibkey:
Cite (ACL):
Andrew Piper, Michael Xu, and Derek Ruths. 2024. The Social Lives of Literary Characters: Combining citizen science and language models to understand narrative social networks. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 472–482, Miami, USA. Association for Computational Linguistics.
Cite (Informal):
The Social Lives of Literary Characters: Combining citizen science and language models to understand narrative social networks (Piper et al., NLP4DH 2024)
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PDF:
https://aclanthology.org/2024.nlp4dh-1.45.pdf