Exploring the Technical Knowledge Interaction of Global Digital Humanities: Three-decade Evidence from Bibliometric-based perspectives

• URL: http://arxiv.org/abs/2508.08347v1
• Date: Mon, 11 Aug 2025 12:27:39 GMT
• Title: Exploring the Technical Knowledge Interaction of Global Digital Humanities: Three-decade Evidence from Bibliometric-based perspectives
• Authors: Jiayi Li, Chengxi Yan, Yurong Zeng, Zhichao Fang, Huiru Wang,
• Abstract summary: This study introduces a new concept of Topic-Method Composition (TMC)<n>TMC refers to a hybrid knowledge structure generated by the co-occurrence of specific research topics and the corresponding method.<n>This study develops a workflow combining bibliometric analysis, topic modeling, and network analysis to analyze the development characteristics and patterns of research disciplines.
• Score: 6.4731966382810215
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital Humanities (DH) is an interdisciplinary field that integrates computational methods with humanities scholarship to investigate innovative topics. Each academic discipline follows a unique developmental path shaped by the topics researchers investigate and the methods they employ. With the help of bibliometric analysis, most of previous studies have examined DH across multiple dimensions such as research hotspots, co-author networks, and institutional rankings. However, these studies have often been limited in their ability to provide deep insights into the current state of technological advancements and topic development in DH. As a result, their conclusions tend to remain superficial or lack interpretability in understanding how methods and topics interrelate in the field. To address this gap, this study introduced a new concept of Topic-Method Composition (TMC), which refers to a hybrid knowledge structure generated by the co-occurrence of specific research topics and the corresponding method. Especially by analyzing the interaction between TMCs, we can see more clearly the intersection and integration of digital technology and humanistic subjects in DH. Moreover, this study developed a TMC-based workflow combining bibliometric analysis, topic modeling, and network analysis to analyze the development characteristics and patterns of research disciplines. By applying this workflow to large-scale bibliometric data, it enables a detailed view of the knowledge structures, providing a tool adaptable to other fields.

Related papers

• Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
  We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
  arXiv  Detail & Related papers  (2026-01-21T16:47:05Z)
• AI Sprints: Towards a Critical Method for Human-AI Collaboration [0.0]
  This article introduces the possibility for new forms of humanistic inquiry through what I term 'AI sprints'<n>I demonstrate how tight loops of iterative development can adapt data and book sprint methodologies whilst acknowledging the profound transformations generative AI introduces.<n>The paper contributes both a practical methodology for intensive AI-augmented research and a theoretical framework for understanding the transformations of this hybrid method.
  arXiv  Detail & Related papers  (2025-12-13T15:56:11Z)
• Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation [0.0]
  We argue that new technologies hold potential to address longstanding methodological challenges when deployed with knowledge, purpose, and ethical commitment.<n>We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand--rather than displace--the irreducible insights of qualitative research.
  arXiv  Detail & Related papers  (2025-09-15T23:12:13Z)
• A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora [6.384357773998868]
  Sci-OG is a semi-auto-mated methodology for generating research topic.<n>This paper presents Sci-OG, a semi-auto-mated methodology for generating research topic.<n>We evaluate this approach against a range of alternative solutions using a dataset of 21,649 manually annotated semantic triples.
  arXiv  Detail & Related papers  (2025-08-06T08:48:14Z)
• A Survey of Deep Learning for Geometry Problem Solving [72.22844763179786]
  This paper provides a survey of the applications of deep learning in geometry problem solving.<n>It includes (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; and (iii) a detailed analysis of evaluation metrics and methods.<n>Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field.
  arXiv  Detail & Related papers  (2025-07-16T06:03:08Z)
• The Blind Men and the Elephant: Mapping Interdisciplinarity in Research on Decentralized Autonomous Organizations [0.0]
  Decentralized Autonomous Organizations (DAOs) are attracting interdisciplinary interest, particularly in business, economics, and computer science.<n>Research remains fragmented across disciplines, limiting a comprehensive understanding of their potential.<n>Current research remains predominantly applied and case-driven, with limited theoretical integration.
  arXiv  Detail & Related papers  (2025-02-14T07:06:43Z)
• Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review [3.0712840129998513]
  This literature review proposes a taxonomy and framework that encapsulates recent methodological advances in this field. We introduce a novel data fusion category -- mid fusion -- and a graph-based technique for refining literature reviews, termed citation graph pruning. There remains a need for further research to bridge the divide between multimodal learning and training studies and foundational AI research.
  arXiv  Detail & Related papers  (2024-08-22T22:42:23Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Onologies are widely used for representing domain knowledge and meta data.<n> logical reasoning that can directly support are quite limited in learning, approximation and prediction.<n>One straightforward solution is to integrate statistical analysis and machine learning.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Digital Transformation of Education, Systems Approach and Applied Research [0.0]
  This article proposes the construction of a systemic model of digital education as part of research applied to public policy. Considering the digital domain in its pervasiveness, it highlights the importance of a complex approach to understanding the transformation of practices.
  arXiv  Detail & Related papers  (2024-04-10T07:45:29Z)
• Deep Learning for Human Parsing: A Survey [54.812353922568995]
  We provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing. We introduce five insightful categories: (1) structure-driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efficient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, and (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts.
  arXiv  Detail & Related papers  (2023-01-29T10:54:56Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.