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Topics in dynamic research communities: An exploratory study for the field of information retrieval

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  • Yan, Erjia
  • Ding, Ying
  • Milojević, Staša
  • Sugimoto, Cassidy R.

Abstract

Research topics and research communities are not disconnected from each other: communities and topics are interwoven and co-evolving. Yet, scientometric evaluations of topics and communities have been conducted independently and synchronically, with researchers often relying on homogeneous unit of analysis, such as authors, journals, institutions, or topics. Therefore, new methods are warranted that examine the dynamic relationship between topics and communities. This paper examines how research topics are mixed and matched in evolving research communities by using a hybrid approach which integrates both topic identification and community detection techniques. Using a data set on information retrieval (IR) publications, two layers of enriched information are constructed and contrasted: one is the communities detected through the topology of coauthorship network and the other is the topics of the communities detected through the topic model. We find evidence to support the assumption that IR communities and topics are interwoven and co-evolving, and topics can be used to understand the dynamics of community structures. We recommend the use of the hybrid approach to study the dynamic interactions of topics and communities.

Suggested Citation

  • Yan, Erjia & Ding, Ying & Milojević, Staša & Sugimoto, Cassidy R., 2012. "Topics in dynamic research communities: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 6(1), pages 140-153.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:1:p:140-153
    DOI: 10.1016/j.joi.2011.10.001
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    3. Beibei Hu & Xianlei Dong & Chenwei Zhang & Timothy D. Bowman & Ying Ding & Staša Milojević & Chaoqun Ni & Erjia Yan & Vincent Larivière, 2015. "A lead-lag analysis of the topic evolution patterns for preprints and publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2643-2656, December.
    4. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).
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    9. Chankook Park & Minkyu Kim, 2021. "A Study on the Characteristics of Academic Topics Related to Renewable Energy Using the Structural Topic Modeling and the Weak Signal Concept," Energies, MDPI, vol. 14(5), pages 1-24, March.
    10. Chaoqun Ni & Cassidy R. Sugimoto & Blaise Cronin, 2013. "Visualizing and comparing four facets of scholarly communication: producers, artifacts, concepts, and gatekeepers," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1161-1173, March.
    11. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
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    18. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    19. Jian Xu & Ying Ding & Yi Bu & Shuqing Deng & Chen Yu & Yimin Zou & Andrew Madden, 2019. "Interdisciplinary scholarly communication: an exploratory study for the field of joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1597-1619, June.
    20. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.

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