Computational Method in Literary Studies

This study examines the development of Computational Literary Studies (CLS) between 2000 and 2025, tracing its publication patterns, leading contributors, geographical reach, funding landscape, and thematic directions. Drawing on the Scopus database, the analysis focuses on 295 English-language journal articles and applies scientific performance measures together with science-mapping techniques. Co-occurrence analysis, full counting, and network visualization generated through VOSviewer provide the basis for identifying trends in the field. The results indicate a significant increase in publication activity after 2019, suggesting a growing reliance on computational tools within literary scholarship. Digital Scholarship in the Humanities appears to be the most active publication venue, with the United States contributing the largest share of research output, followed by several European countries and China. Patterns of institutional affiliation and funding indicate a strong presence of European research bodies, particularly Horizon 2020 and the European Research Council. The thematic mapping points to two broad orientations: one centered on technologies such as Natural Language Processing (NLP), deep learning, and language models, and another grounded in humanities-based approaches, including digital humanities, distant reading, and stylometry. Transitional clusters—most notably, text mining and sentiment analysis—bridge these orientations, illustrating the increasing convergence between computational techniques and literary interpretation. Overall, the study offers a detailed overview of the CLS research landscape and provides an evidence-based reference for shaping research priorities, curriculum design in digital humanities, and future scholarly directions.