Plain-language explanation.
Digital humanities applies computational tools and methods to humanities research — including history, literature, linguistics, and cultural studies. It enables scholars to analyse texts, images, and cultural artefacts at scales impossible through traditional close reading.
Core concepts and standard treatment.
Digital humanities methods include text mining and NLP (named entity recognition, sentiment analysis, topic modelling using LDA), corpus linguistics, network analysis of historical correspondences, digital mapping (GIS for spatial humanities), image analysis (iconographic databases), and building digital critical editions and archives.
Deeper theory, debates and edge cases.
Advanced digital humanities engages with computational approaches to literary history (distant reading — Moretti), digitisation and OCR quality challenges, FAIR data principles for humanities datasets, linked open data and the Semantic Web (CIDOC CRM ontology), and debates about the reproducibility and interpretability of computational humanities findings.
How it is applied in practice.
At the professor and digital research infrastructure level, digital humanities scholars lead projects such as the Oxford Text Archive, Programming Historian, and DARIAH; design data standards for cultural heritage (EAD, Dublin Core, IIIF); secure AHRC/NEH funding for digital editions; and supervise interdisciplinary research combining ML with archival science.