The Social Lives of Literary Characters: Combining citizen science and language models to understand narrative social networks (2024)

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.

## introduction characters and their interactions are a fundamental feature of storytelling. as a prominent dimension of cognitive literary theory has argued, fictional characters provide readers with the opportunity to identify with other imaginary human beings and model social relationships @xcite @xcite @xcite . according to this theory, the interactions among characters and the resulting social networks provide an important training ground for the development of social cognition (a.k.a. theory of mind) @xcite . well over a decade ago, a robust body of work began to emerge in nlp to address the extraction of social networks from narrative texts @xcite @xcite . that work established important methodological foundations for the study of literary social networks, which were understood to be a key component of research in the digital humanities @xcite . nevertheless, this work was faced with two significant challenges: 1) automated methods never exhibited robust levels of accuracy to be applicable to real-world cases; 2) the costliness of deriving high-quality training data made it difficult to build more accurate models. in this paper, we aim to address these challenges to further the study of narrative social networks by focusing on the following methodological contributions: here we demonstrate a proof-of-concept analysis of the ways in which social networks in stories are shaped by genre and audience factors such as fictionality, cultural context, prestige, and expected reader age. we conclude with a discussion of areas for future work. ## prior work some of the earliest theoretical work concerning the value of social networks for literature was undertaken by @xcite and @xcite . @xcite 's theory of character-space has been particularly influential. this theory highlights the skewed distribution of attention around a primary character, referred to as 'the one and the many' structure by woloch. this work established an important theoretical framework for studying character relations in addition to individual characters @xcite . early methodological work on the extraction of character networks was undertaken by several researchers @xcite @xcite . much of this work focused on the use of sentence-level co-occurrence or subjectverb-object triplets as the foundation of building character interactions. @xcite provide an extensive survey of methods of social network extraction applied to cultural texts (i.e. stories and screenplays), ranging from interaction identification to network analysis methods. in terms of applications, mac carron and kenna (2012) analyzed character networks within three european epics (the iliad, beowulf, tain bo cuillange) to understand their relation to contemporary real-world social networks. volker and smeets (2020) compared fictional networks in dutch literature with real-world networks with respect to racial groups. and ardanuy and sporleder (2014) and @xcite used social networks as a mechanism to detect book genres. dialogue networks have also been studied as a subset of literary social networks @xcite , with a similar principal applied to the study of drama (algee-hewitt, 2017; @xcite . finally, substantial work has focused on the detection of relationship types (instead of individual interactions), such as kinship ties @xcite @xcite and conflict groups @xcite . ## analysis in this section, we aim to illustrate the potential utility of our data for the large-scale study of cultures of storytelling. we measure the effects of the following four stylistic and audience categories on the distribution of character interaction types and the resulting book-level social networks: fictionality. here we test for the effects that fiction has on social interactions. for interactions we look at all adult genres. for social networks, we sample books from the prizewinners and bestsellers categories for fiction and biography for nonfiction. world. we test for the effects that books published in non-western cultures have on social interactions. specifically we look at books published in english in india, south africa, and nigeria that were reviewed in major literary reviews in their respective regions and compare them to our western prizewinner category. prestige. prior work has identified strong stylistic differences between best-selling fiction and fiction written to appeal to literary elites on prize committees @xcite . here we test whether these findings extend to social interactions and their resulting networks. ## conclusion understanding narratives at large scale is a core concern of the digital humanities @xcite . the social interactions of characters @xcite and the resulting social networks @xcite been theorized as important dimensions of human storytelling. in this paper we have endeavored to illustrate the potential that citizen science has as a means of generating data for training and testing language models towards the goal of understanding the social lives of characters. with respect to citizen science as a mechanism of crowd-sourced text annotation, we find annotation quality on par with trained student annotators. as prior work has suggested, citizen science projects achieve the same quality standards as other approaches and bring with them the affordances of a volunteer, community-based approach to scientific discovery @xcite . we hope more projects in nlp and dh will utilize this significant resource. with respect to narrative understanding, we have identified two notable findings in our work. first, fictional narratives strongly favor embodied forms of interactions such as physical contact and sensory perception in support of prior work @xcite . second, fictional narratives also strongly favor far denser and less modular social networks. the physicality of relations between characters is amplified by the overall connectedness of characters @xcite . we could add here one additional negative finding: social networks built around individual interaction types do not appear to differ from the overall narrative social networks to which they belong. social networks built around contact, communication, observation, etc. follow the same patterns as the full network. so while it appears interaction types are useful for distinguishing fictional narratives, they do not contribute much to our understanding of the larger social network structures. we also highlight a number of areas for future work: both our slm and gpt indicate significant limitations with respect to the detection of noninteractions, which has also been demonstrated with respect to grammatical models @xcite . much of this can be attributed to the fuzzy boundary around the concept of "interaction"when two characters are grammatically proximate the rejection of their interaction depends on a number of subtle factors (hypotheticality, co-presence versus interaction, etc.). future work will want to further explore this boundary in particular. a second key area is the validation of the social networks themselves. book-level data on narrative social networks remains a costly endeavor. to date, the field still lacks reasonably sized ground truth when it comes to validating book-level social networks. while we show that our constructed social networks from local interactions align well with theoretical expectations, further validation of their accuracy awaits. finally, while we introduce a novel interaction framework in our work, future work will want to think about further nuance with respect to labeling interactions. our work does not address the valence of interactions, an important property of narrative relations @xcite , nor does it address overall relationship types (such as kinship or narrative properties such as antagonist, etc.). these too can be valuable frameworks for understanding the structural properties of narratives. we hope that our publicly shared training data and slm can be useful tools for researchers to further study the nature of narrative social networks.