About this Research Topic

Background

Digital Humanities (DH) is a dynamic and interdisciplinary field that has redefined the creation, dissemination, and exploration of knowledge in the humanities in the digital era. DH empowers researchers with new tools in order to address new research questions while engaging with vast and diverse datasets or collections. It also offers the instruments to assess and reevaluate traditional studies in light of modern computational advancements.

The main objective of this Research Topic article collection is to highlight the importance of integrating Machine Learning (ML) into DH and its impact on research within the field. This Research Topic endeavors to uncover the transformative potential of computational methodologies and tools in redefining traditional research models adopted in the humanities. Moreover, it seeks to explore the potential challenges and ethical considerations that emerge when adopting technology-driven approaches in the study of cultural heritage, literature, history, and other humanistic disciplines.

We invite researchers to submit their cutting-edge work that showcases the diverse ways ML facilitates collaborative and transdisciplinary research while extending the horizons of knowledge production and distribution. We expect a wide range of participation from the different subfields, from the application of Natural Language Processing (NLP) for textual analysis, enabling deeper insights into literary works and historical documents, to Computer Vision techniques in order to classify visual content within digital libraries and archives, enhancing the accessibility and organization of visual resources. Contributions exploring ML-based recommender systems and personalized content suggestions in digital libraries and archives are also encouraged, as they optimize user experiences and engagement.

For this Research Topic, we invite contributions that are within this non-exhaustive list of topics:
• Text Summarization for Large Text Collections
• Image Analysis for Document Classification
• Collaborative Filtering for Data Curation
• Deep Learning for Document Restoration
• Personalized Recommender Systems
• Cross-Lingual Information Retrieval
• Knowledge Organization
• Responsible AI Systems
• Cultural Heritage Collections

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: digital humanities, machine learning, text summarization, computer vision, natural language processing, document restoration, deep learning, personalized recommendation systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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