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Evolutionary exploration and comparative analysis of the research topic networks in information disciplines

Author

Listed:
  • Xiaoguang Wang

    (Wuhan University)

  • Hongyu Wang

    (Wuhan University
    The University of Texas at Austin)

  • Han Huang

    (Wuhan University)

Abstract

“Information Science and Library Science” (LIS) and “Computer Science” (CS) are two information-related disciplines with similar academic context and culture, and their research topics are moderately associated. To uncover the differences and similarities of their research topics in the aspect of dynamic distribution, and explore the future development state of relevant research, this study collected a set of scientific papers from 2014 to 2019 on the Web of Science to construct the co-keyword network sequences for the disciplines of LIS and CS respectively. The networking topology and evolutionary context of research topics in recent research of these two information-related disciplines were analyzed through a self-developed visualization tool for network evolution analysis—NEViewer. This study suggests that CS pays more attention to the studies of information technologies, while LIS focuses more on the communication, organization, access, and use of information. Meanwhile, based on the perspective of discipline-comparative, the development patterns in these two disciplines were summarized. That is, the research connotation of CS is more concentrated while the research denotation of LIS is more extensive, and the research hotspots of LIS have shifted slightly faster, while the continuity of the research development in CS is slightly higher. The results reported in this study also show that there is moderate interdisciplinarity in the research and application of information technology between these two disciplines, which indicates that some increasingly mature technologies in CS will be more deeply applied in the future research of LIS.

Suggested Citation

  • Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:6:d:10.1007_s11192-021-03963-6
    DOI: 10.1007/s11192-021-03963-6
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    References listed on IDEAS

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    3. Kaiwen Shi & Kan Liu & Xinyan He, 2024. "Heterogeneous hypergraph learning for literature retrieval based on citation intents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4167-4188, July.
    4. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).

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