Towards automated analysis of research methods in library and information science

Previous studies of research methods in Library and Information Science (LIS) lack consensus in how to define or classify research methods, and there have been no studies on automated recognition of research methods in the scientific literature of this field. This work begins to fill these gaps by studying how the scope of “research methods” in LIS has evolved, and the challenges in automatically identifying the usage of research methods in LIS literature. A total of 2,599 research articles are collected from three LIS journals. Using a combination of content analysis and text mining methods, a sample of this collection is coded into 29 different concepts of research methods and is then used to test a rule-based automated method for identifying research methods reported in the scientific literature. We show that the LIS field is characterized by the use of an increasingly diverse range of methods, many of which originate outside the conventional boundaries of LIS. This implies increasing complexity in research methodology and suggests the need for a new approach towards classifying LIS research methods to capture the complex structure and relationships between different aspects of methods. Our automated method is the first of its kind in LIS, and sets an important reference for future research.