# Computational Representations of Character Significance in Novels

arXiv: https://arxiv.org/abs/2601.15508 · HTML: https://mlsanity.com/paper/2601.15508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics](https://mlsanity.com/paper/1812.08092/) (2018)
- [Modeling Naive Psychology of Characters in Simple Commonsense Stories](https://mlsanity.com/paper/1805.06533/) (2018)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Measuring Information Propagation in Literary Social Networks](https://mlsanity.com/paper/2004.13980/) (2020)
- [Grounding Characters and Places in Narrative Texts](https://mlsanity.com/paper/2305.17561/) (2023)
- [Mining and modeling character networks](https://mlsanity.com/paper/1608.00646/) (2016)
- [The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction](https://mlsanity.com/paper/2412.11560/) (2024)
- [A City of Millions: Mapping Literary Social Networks At Scale](https://mlsanity.com/paper/2502.19590/) (2025)
- [Literary Evidence Retrieval via Long-Context Language Models](https://mlsanity.com/paper/2506.03090/) (2025)

## Built on by (most cited first, 1 of 1)

- [GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study](https://mlsanity.com/paper/2605.28643/) (2026)

## Abstract (arXiv)

Characters in novels have typically been modeled based on their presence in scenes in narrative, considering aspects like their actions, named mentions, and dialogue. This conception of character places significant emphasis on the main character who is present in the most scenes. In this work, we instead adopt a framing developed from a new literary theory proposing a six-component structural model of character. This model enables a comprehensive approach to character that accounts for the narrator-character distinction and includes a component neglected by prior methods, discussion by other characters. We compare general-purpose LLMs with task-specific transformers for operationalizing this model of character on major 19th-century British realist novels. Our methods yield both component-level and graph representations of character discussion. We then demonstrate that these representations allow us to approach literary questions at scale from a new computational lens. Specifically, we explore Woloch's classic "the one vs the many" theory of character centrality and the gendered dynamics of character discussion.