Digital Humanities and English Literary Heritage: Mapping Cultural Narratives Through Computational Methods

Abstract

Digital humanities has transformed literary scholarship by enabling researchers to analyse textual collections at scales beyond conventional close reading. The present study examines how computational methods can be used to map cultural narratives embedded in English literary heritage across more than two centuries. A corpus of 240 English-language literary works published between 1800 and 2020 was organised into six historical periods and analysed through text mining, topic modelling, named-entity recognition, geographical mapping, lexical analysis, and character-network analysis. The corpus contained approximately 21.84 million word tokens. An eight-topic Latent Dirichlet Allocation model identified recurrent narrative formations related to class and social hierarchy, gender and domesticity, industrial modernity, empire and colonial encounter, war and national identity, religion and morality, migration and belonging, and individual identity. Significant historical variation was observed in several thematic categories. The mean normalized thematic-diversity score increased from .64 in the earliest period to .78 in the contemporary period, F(5,234) = 12.46, p < .001. Geographic dispersion was positively associated with thematic diversity (r = .44, p < .001), while character-network centralization declined across historical periods, suggesting increasingly distributed narrative structures. Manual validation of a 20% subsample produced substantial agreement with computationally identified dominant themes (κ = .76). The study demonstrates that computational methods can extend rather than replace interpretive literary scholarship by exposing large-scale relationships among historical period, place, character structure, and cultural discourse. It concludes that digital humanities offers a rigorous framework for preserving, mapping, and reinterpreting English literary heritage when quantitative modelling remains connected to contextual and close textual analysis.