IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i7d10.1007_s11192-024-05058-4.html
   My bibliography  Save this article

A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach

Author

Listed:
  • Abhijit Thakuria

    (Gauhati University)

  • Dipen Deka

    (Gauhati University)

Abstract

The study utilized Latent Dirichlet Allocation (LDA) Topic modeling to identify prevalent latent topics within Open Access (OA) Library and Information Science (LIS) journals from 2013 to 2022. Eight core OA Scopus indexed journals were selected based on their SJR scores and DOAJ listing. Titles, Abstracts and keywords of 2589 articles were extracted from the Scopus database. R software packages were used to perform data analysis and LDA topic modeling. The optimal value of k was determined to be 9. The analysis revealed that 53.89% of documents comprise over 50% of a certain topic (θ > 0.50). Notably, ‘Scholarly Communication’ and ‘Information Systems, Models and Frameworks’ emerged as dominant topics with the highest proportions of research literature in the corpus. The topic ‘Scholarly Communication’ experienced significant growth with an average annual growth rate (AAGR) of 4.37%, while ‘Collection development and E-resources’ exhibited the lowest research proportion and a negative AAGR of − 4.22%. Additionally, topics such as ‘Information Seeking Behaviour and User Studies’, ‘User Education and Information Literacy’, and ‘Information Retrieval and Systematic Review’ remained stable and persistent topics. Conversely, research on traditional topics like ‘Librarianship and Profession’, ‘Bibliometrics’ and ‘Medical Library and Health Information’ showed a gradual decline. The LDA topic modeling approach unveiled previously unknown or unexplored topics in open access LIS research literature, enhancing our understanding of emerging trends.

Suggested Citation

  • Abhijit Thakuria & Dipen Deka, 2024. "A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3841-3869, July.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:7:d:10.1007_s11192-024-05058-4
    DOI: 10.1007/s11192-024-05058-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-024-05058-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-024-05058-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    2. Jinxuan Ma & Brady Lund, 2021. "The evolution and shift of research topics and methods in library and information science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1059-1074, August.
    3. Erjia Yan, 2015. "Research dynamics, impact, and dissemination: A topic-level analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2357-2372, November.
    4. Leslie Chan, 2006. "Open Access Archiving: The Fast Track to Building Research Capacity in Developing Countries," Working Papers id:527, eSocialSciences.
    5. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    6. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    7. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
    8. Cassidy R. Sugimoto & Daifeng Li & Terrell G. Russell & S. Craig Finlay & Ying Ding, 2011. "The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 185-204, January.
    9. Yan, Erjia, 2014. "Research dynamics: Measuring the continuity and popularity of research topics," Journal of Informetrics, Elsevier, vol. 8(1), pages 98-110.
    10. Dangzhi Zhao & Andreas Strotmann, 2014. "The knowledge base and research front of information science 2006–2010: An author cocitation and bibliographic coupling analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 995-1006, May.
    11. Yu-Wei Chang & Mu-Hsuan Huang & Chiao-Wen Lin, 2015. "Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2071-2087, December.
    12. Javier De la Hoz-M & Mª José Fernández-Gómez & Susana Mendes, 2021. "LDAShiny: An R Package for Exploratory Review of Scientific Literature Based on a Bayesian Probabilistic Model and Machine Learning Tools," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    13. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    14. Bhaskar Mukherjee, 2009. "Scholarly research in LIS open access electronic journals: A bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 167-194, July.
    15. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    16. Otto Tuomaala & Kalervo Järvelin & Pertti Vakkari, 2014. "Evolution of library and information science, 1965–2005: Content analysis of journal articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(7), pages 1446-1462, July.
    17. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    18. Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
    19. Yu-Wei Chang & Mu-Hsuan Huang, 2012. "A study of the evolution of interdisciplinarity in library and information science: Using three bibliometric methods," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 22-33, January.
    20. Yu‐Wei Chang & Mu‐Hsuan Huang, 2012. "A study of the evolution of interdisciplinarity in library and information science: Using three bibliometric methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 22-33, January.
    21. Carlos G. Figuerola & Francisco Javier García Marco & María Pinto, 2017. "Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1507-1535, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu-Wei Chang, 2018. "Examining interdisciplinarity of library and information science (LIS) based on LIS articles contributed by non-LIS authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1589-1613, September.
    2. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    3. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    4. Carlos G. Figuerola & Francisco Javier García Marco & María Pinto, 2017. "Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1507-1535, September.
    5. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    6. 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.
    7. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    8. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    9. Pertti Vakkari & Yu-Wei Chang & Kalervo Järvelin, 2022. "Largest contribution to LIS by external disciplines as measured by the characteristics of research articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4499-4522, August.
    10. María Pinto & Rosaura Fernández-Pascual & David Caballero-Mariscal & Dora Sales, 2020. "Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1479-1510, August.
    11. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    12. 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.
    13. Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
    14. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    15. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    16. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
    17. Ping Liu & Qiong Wu & Xiangming Mu & Kaipeng Yu & Yiting Guo, 2015. "Detecting the intellectual structure of library and information science based on formal concept analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 737-762, September.
    18. Qian-Jin Zong & Hong-Zhou Shen & Qin-Jian Yuan & Xiao-Wei Hu & Zhi-Ping Hou & Shun-Guo Deng, 2013. "Doctoral dissertations of Library and Information Science in China: A co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 781-799, February.
    19. Sabrina Petersohn & Thomas Heinze, 2018. "Professionalization of bibliometric research assessment. Insights from the history of the Leiden Centre for Science and Technology Studies (CWTS)," Science and Public Policy, Oxford University Press, vol. 45(4), pages 565-578.
    20. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.

    More about this item

    Keywords

    Topic modeling; LDA; Open access; LIS;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:129:y:2024:i:7:d:10.1007_s11192-024-05058-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.