Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities

2026-06-23

AI Summary

This study investigates how the data-driven paradigm has reshaped the evolutionary trajectory of research methodologies in Library and Information Science (LIS). Drawing on LIS publications from 1990 to 2022, the authors employ natural language processing techniques to automatically extract fine-grained methodological entities—specifically algorithmic models, data resources, software tools, and evaluation metrics—and combine temporal analysis with topic clustering to uncover dynamic patterns across three dimensions: temporal evolution, thematic variation, and methodological interdisciplinarity. This work offers the first systematic characterization of LIS methodological evolution from the perspective of fine-grained method entities, revealing data resources as a central driving force and identifying a recurring “emergence–stabilization/adoption” cycle that provides novel empirical evidence for understanding methodological transformation in the field.

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 resources, software and tools, and metrics. Based on these entities, the study examines the evolution of LIS research methods from three dimensions: the characteristics of research method entities over time, their evolution within different research topics, and the evolutionary features of research method entities across various research methods. The findings highlight data resources as a pivotal driver of methodological evolution in LIS, revealing a cyclical pattern of "emergence-stability/practical application" in the development of research methods within the field.

Problem

• data-driven research
• Library and Information Science
• research methods evolution
• method entities
• data resources

Innovation

• data-driven research
• fine-grained method entities
• methodological evolution
• Library and Information Science
• data resources