Culturomics: Reflections on the potential of big data discourse analysis methods for identifying research trends

Abstract

This study examines the potential of big data discourse analysis (i.e., culturomics) to produce valuable knowledge, and suggests a mixed methods model for improving the effectiveness of culturomics. We argue that the importance and magnitude of using qualitative methods as complementing quantitative ones, depends on the scope of the analyzed data (i.e., the volume of data and the period it spans over). We demonstrate the merit of a mixed methods approach for culturomics analyses in the context of identifying research trends, by analyzing changes over a period of 15 years (2000-2014) in the terms used in the research literature related to learning technologies. The dataset was based on Google Scholar search query results. Three perspectives of analysis are presented: (1) Curves describing five main types of relative frequency trends (i.e., rising; stable; fall; rise and fall; rise and stable); (2) The top key-terms identified for each year; (3) A comparison of data from three datasets, which demonstrates the scope dimension of the mixed methods model for big data discourse analysis. This paper contributes to both theory and practice by providing a methodological approach that enables gaining insightful patterns and trends out of culturomics, by integrating quantitative and qualitative research methods.