The Architecture of Fictionality: A Computational Analysis of Narrative Divides
Abstract
Fictionality, as both a literary construct and a marker of cultural imagination, defines the shifting boundaries between storytelling and documentary representation. This study examines how narrativity and genre features distinguish fiction from nonfiction in contemporary literature. Drawing on narratological theory, we analyze how texts create worlds, build suspense, and organize events—central strategies of storytelling—using computational distant reading applied to a large-scale English-language corpus. Narrativity is operationalized via quantifiable features: staging (the introduction of settings and characters), plot progression (the unfolding of events), and cognitive tension (the management of suspense and uncertainty). Our three-stage analysis begins with segment-level statistical comparisons, which reveal that fiction and nonfiction deploy narrativity in systematically different ways across narrative arcs. Next, statistical tests demonstrate that, beyond the fiction/nonfiction binary, specific genres display distinct narrativity profiles, confirming the diversity of fictional forms. Finally, supervised machine learning shows that these narrativity features reliably predict fictionality, underscoring their explanatory power. Together, these computational findings demonstrate that narrativity and its constituent elements are central to fiction’s uniqueness as a literary form. This research enables a valid comparative analysis and also demonstrates that narrative arcs in both fiction and nonfiction are computationally predictable. Ultimately, our study affirms the value of computational methods for modeling the formal features of fiction, which offers fresh insights into the architecture of storytelling and reinforces narrativity’s central role in literary studies.
Keywords
Fictionality, narrativity, Computational Literary Analysis, Genre Classification, Distant Reading
Ethical Statement
I hereby declare that all phases of the study comply with research and publication ethics, and that I have adhered to ethical rules and the principles of scholarly citation.
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