Visualizing systematic literature reviews to identify new areas of research

Abstract

This research paper describes the application of linguistic analysis through scaled co-occurrence networks to create visual representations of systematic literature reviews. This approach uses currently available and free tools in this new application. Co-occurrence networks are a method for visualizing relationships between concepts within written material. The results of a co-occurrence analysis are a network that reflects the relationships between words based on meaning similarities among words and text segments. To illustrate this approach, I applied the methodology of a systematic literature review search that resulted in 391 unique journal articles, books, and reports on identity published in the period from 1995 to 2015. The abstracts, titles, and keywords of these journals were analyzed via linguistic analysis to create a word co-occurrence network of these articles related to identity in science and engineering. Clusters within this network were identified based on word frequency. The results of this research illustrate a hole in the current identity literature in understanding diversity beyond traditional definitions of race, class, and gender. This method has the potential to powerfully convey and embed information about large amounts of data in a single image and offers a new way to report findings from a systematic literature review.