This article presents a computational analysis of thematic and methodological developments in the field of digital humanities (DH) over the past two decades. Drawing on a corpus of 711 abstracts from Digital Humanities Quarterly (2007–25), the study employs text mining and topic modelling to map intellectual trends, research methods and conceptual priorities that have shaped the discipline’s evolution. Using Latent Dirichlet Allocation (LDA) and co-occurrence analysis implemented in R, the research identifies 20 dominant topics, ranging from digital text analysis and pedagogy to cultural heritage and software infrastructure. The longitudinal distribution of these topics reveals both the persistence of core themes and the emergence of new methodological orientations, particularly the integration of machine learning, data visualization and multimodal research. The results indicate an increasing convergence between computational and interpretative approaches, reflecting DH’s gradual transformation from tool-oriented experimentation to theory-informed analysis. By offering a reproducible framework for large-scale bibliometric text analysis, this study contributes to a more systematic understanding of how DH research agendas evolve across time and across modes of scholarly communication.
Aladağ et al. (Sun,) studied this question.
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