Catalan crime fiction has grown rapidly in the last 20 years, becoming one of contemporary Catalan literature's most dynamic genres. This article examines its evolution from a geocritical perspective, focusing on the diversification of narrative spaces between 1954 and 2023. While Barcelona as a location has traditionally dominated Catalan noir, the genre has increasingly shifted towards other locations. Using quantitative and qualitative methods-including text mining, geoparsing, GIS visualization and close textual analysis-this study analyses 92 works to trace these shifts. Interrogating prior scholarship highlighting the role of Pep Coll's L'abominable crim de l'Alsina Graells (1999) in such a shift, this article demystifies its importance and identifies two major periods of decentralization instead: the 1980s, with Jaume Fuster and Maria Ant & ograve;nia Oliver highlighting the Pais Valenci & agrave; and Illes Balears, and the 2010s, marked by broader and spatially diversifying production by authors like Margarita Aritzeta, Teresa Solana and Jordi de Manuel. The study demonstrates GIS's value in complementing traditional literary analysis, showing how macro-level spatial trends refine geocritical theories. By integrating distant and close reading, it offers a methodological blueprint for future research, advancing understanding of Catalan noir's geographical evolution and the interplay of space, literature and culture.
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.
This article presents a bibliometric analysis of digital humanities (DH) research in China by examining its growth, thematic focus, institutional contributions and intellectual structures. Using 289 publications indexed from 2004 to 2025, data were analysed using Biblioshiny to assess authorship patterns, citation impact, collaboration networks and thematic clusters. The results show a rapid annual growth rate of 20.92 per cent, reflecting the field's expansion. Leading institutions include Peking University, Zhejiang University and Nanjing University, with scholars such as Jun Wan, Hao Wang and Xiaoguang Wang identified as highly influential. The keywords-digital humanities visualization and deep learning-highlight computational approaches and the preservation of cultural heritage. Although international collaboration is increasing, research is primarily driven by domestic partnerships. Key features include a concentration in elite universities, interdisciplinary integration, a domestically centred but internationally emerging collaboration network, and limited attention to ethical and methodological issues such as data privacy and censorship. This study provides one of the first systematic overviews of China's DH landscape and its growing significance.
We evaluate a Stanza-based pipeline for Old English that combines character-level processing with language-specific word embeddings derived from the Dictionary of Old English Corpus. On a 25,000-word dataset annotated with Universal Dependencies, character-level models yield consistent gains over token-based variants for lemmatization and dependency parsing (test UAS 88.92 per cent; LAS 79.65 per cent). Relative to a multilingual baseline for Old English, our best parser improves LAS by approximately 20 per cent; compared with a recent monolingual spaCy baseline on similar data sizes, gains are approximately 5 per cent on our split. We complement scores with qualitative analyses to illustrate strengths and limitations, and we provide implementation details to support replication. The main conclusion of this work is that, under low-resource conditions, language-specific embeddings and character-level modelling are more effective for Old English processing than cross-linguistic transfer learning.
Based on my opening keynote at DH2025, this article examines the impact that transformer-based machine learning brings to the interpretive work of historians. The discussion begins by revisiting an earlier phase of my research, which focused on digitally mediated, multiscale analysis-or what I refer to as 'digital (re)reading' - using database queries to detect structural patterns in digitized historical sources. Building on this foundation, I turn to the present, where my team and I investigate how large language models (LLMs) and vision-language models (VLMs) can support algorithmic reading across heterogeneous and semantically complex corpora. This new phase of inquiry explores how such models enable semantic, stylistic, sentiment and multimodal analysis, moving decisively beyond the constraints of keyword search and frequentist approaches. The article also introduces the DeepPast initiative, a modular artificial intelligence (AI) framework designed to promote the use of pluggable, task-specific components that run on low-power hardware rather than hyperscale, monolithic systems. The DeepPast architecture supports multiple interpretive modes, allowing historians to engage in a structured and purposeful dialogue with an AI assistant that functions as a genuine research partner. The discussion concludes with reflections on how to keep historians in the loop and to produce AI-assisted historical research marked by greater interpretive nuance and sophistication.
