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

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  • 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
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    Keywords

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

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