Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities
arXiv:2606.25320v1 Announce Type: cross Abstract: Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain. Using academic papers from LIS published between 1990 and 2022, four key categories of data-driven method entities are automatically extracted: algorithms and models, data reso
The proliferation of big data and advanced information technology necessitates a data-driven analysis of how research fields themselves are evolving.
This study offers a meta-perspective on the methodological evolution within Library and Information Science, highlighting how data-centric approaches are reshaping academic disciplines.
Traditional qualitative or mixed-methods research in LIS is increasingly complemented and potentially superseded by data-driven analytical techniques, altering the landscape of scholarly inquiry.
- · Data scientists
- · Academics leveraging big data tools
- · Information science researchers
- · Traditional qualitative researchers
- · Institutions slow to adopt data analytics
- · Disciplines without robust data infrastructure
The LIS field sees a significant increase in research output utilizing data-driven methods.
Educational curricula in LIS and related fields will shift to emphasize computational and data analysis skills.
The integration of AI and machine learning in research methods becomes standard, further blurring lines between computer science and traditional humanities/social sciences.
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