 Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrievalBaitong Chen, Satoshi Tsutsui, Ying Ding and Feicheng Ma Journal of Informetrics, 2017, vol. 11, issue 4, 1175-1189 Abstract: Understanding topic evolution in a scientific domain is essential for capturing key domain developments and facilitating knowledge transfer within and across domains. Using a data set on information retrieval (IR) publications, this paper examines how research topics evolve by analyzing the topic trends, evolving dynamics, and semantic word shifts in the IR domain. Knowledge transfer between topics and the developing status of the major topics have been recognized, which are represented by the merging and splitting of local topics in different time periods. Results show that the evolution of a major topic usually follows a pattern from adjusting status to mature status, and sometimes with re-adjusting status in between the evolving process. Knowledge transfer happens both within a topic and among topics. Word migration via topic channels has been defined, and three migration types (non-migration, dual-migration, and multi-migration) are distinguished to facilitate better understanding of the topic evolution. Keywords: Topic evolution; Semantic word shifts; Content analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:11:y:2017:i:4:p:1175-1189 DOI: 10.1016/j.joi.2017.10.003 Access Statistics for this article Journal of Informetrics is currently edited by Leo Egghe More articles in Journal of Informetrics from Elsevier |
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