Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks

• Published in: Scientometrics Vol. 101; no. 2; pp. 1253 - 1271
• Main Authors: Wang, Xiaoguang, Cheng, Qikai, Lu, Wei
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2014; Springer
• Subjects: Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Computer Science; Detection; Exact sciences and technology; Information and communication sciences; Information science. Documentation; Information Storage and Retrieval; Library and information science. General aspects; Library Science; Methods; Sciences and techniques of general use; Subject fields; Trends; Co-word analysis; Topic evolution; Network communities; Science mapping; Emerging trend detection; Scientific research; Algorithm; Cooccurrence analysis; Coword analysis; Coword
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-014-1347-y

Understanding the evolution of research topics is crucial to detect emerging trends in science. This paper proposes a new approach and a framework to discover the evolution of topics based on dynamic co-word networks and communities within them. The NEViewer software was developed according to this approach and framework, as compared to the existing studies and science mapping software tools, our work is innovative in three aspects: (a) the design of a longitudinal framework based on the dynamics of co-word communities; (b) it proposes a community labelling algorithm and community evolution verification algorithms; (c) and visualizes the evolution of topics at the macro and micro level respectively using alluvial diagrams and coloring networks. A case study in computer science and a careful assessment was implemented and demonstrating that the new method and the software NEViewer is feasible and effective.