๐ค AI Summary
This study investigates the evolution of research method usage and diversity in library and information science (LIS) from 1991 to 2021, along with their dynamic associations with research topics. Drawing on a corpus of over 26,000 journal articles, it pioneers a machine learningโdriven longitudinal content analysis to automatically classify 16 research methods and 18 thematic categories, modeling their co-evolution and presenting findings through an interactive time-series visualization map. The results reveal a paradigmatic shift from conceptual toward empirical approaches and a thematic pivot from system-centered to user-centered foci, accompanied by significant co-evolutionary patterns between methods and topics. This work provides large-scale, fine-grained empirical evidence and analytical tools for understanding methodological transformation in the LIS discipline.
๐ Abstract
The present study analyzed over 26,000 research articles published between 1991 and 2021 in twenty-one major LIS (Library and Information Science) journals, using the machine learning (ML) approach to categorize the research methods used by LIS scholars. The findings of this study are significant. Firstly, there has been a shift in the research strategy from conceptual research (e.g., "Theoretical approach") to empirical research (e.g., "Interview") in LIS investigations over the past 31 years. Secondly, the research topics explored by LIS scholars during this period have moved from system-centered issues (e.g., "Information retrieval/models and algorithms") to user-centered topics (e.g., "Information services "). Thirdly, the study revealed dynamic and revealing relationships between the 18 research topics identified in the study and the 16 research methods commonly adopted in the LIS field. These dynamic relationships can be visualized by year and longitudinally via an interactive map created in this study.