Topic mining and evolution analysis of digital humanities research based on the BERTopic model

Abstract This study employs a deep learning-based topic modeling approach to analyze research topics and evolution trends in digital humanities, offering valuable insights for pertinent research and practices. Literature data were collected from the Web of Science database, with a total of 5,901 valid records analyzed using the BERTopic model and dynamic topic modeling techniques. The study identifies seven major research topics in digital humanities, including digital transformation and interdisciplinary innovation; literary and philosophical studies in digital humanities; cultural heritage, archaeology and semantic technologies; digital pedagogy and artificial intelligence (AI)-enhanced education; social science methods and digital research practice; digital scholarship, libraries, and research infrastructure; and computational literary and linguistic analysis. These topics’ evolution reflects the field’s maturation and the growing prominence of technological advancements, particularly AI and digital pedagogy. The findings underscore how integrating AI, digital tools, and interdisciplinary methods drives the future of digital humanities research. This study enhances the BERTopic model by integrating Sentence-Bidirectional Encoder Representations from Transformers-based embeddings, MultiDimensional Scaling for dimensionality reduction, KMeans clustering, and Log-Likelihood Ratio weighting to enhance topic coherence and accuracy. It reveals the growth of key research areas such as AI-driven pedagogy and digital transformation in digital humanities. The innovation involves utilizing this adjusted model to gain profound insights into the dynamic evolution of research topics, elucidating the influence of digital tools and AI on the field.