Date of this Version

2026

Document Type

Article

Citation

Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 745–750. https://doi.org/10.1109/ICCONS.2017.8250563

Calvo-Bartolomé, L., & Arenas-García, J. (2025). Topic models for decision-support systems, Part II: Expert-in-the-loop hierarchical topic models. Engineering Applications of Artificial Intelligence, 159, 111508. https://doi.org/10.1016/j.engappai.2025.111508

Cao, Q., Cheng, X., & Liao, S. (2023). A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: A case of COVID-19 research. Library Hi Tech, 41(2), 543–569. https://doi.org/10.1108/LHT-03-2022-0144

Chen, Y., Peng, Z., Kim, S.-H., & Choi, C. W. (2023). What we can do and cannot do with topic modeling: A systematic review. Communication Methods and Measures, 17(2), 111–130. https://doi.org/10.1080/19312458.2023.2167965

Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in Sociology, 7, 886498. https://doi.org/10.3389/fsoc.2022.886498

Isoaho, K., Gritsenko, D., & Mäkelä, E. (2021). Topic modeling and text analysis for qualitative policy research. Policy Studies Journal, 49(1), 300–324. https://doi.org/10.1111/psj.12343

Jagannathan, M., Roy, D., & Delhi, V. S. K. (2022). Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies. CSI Transactions on ICT, 10(2), 97–106. https://doi.org/10.1007/s40012-022-00355-w

Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying lda topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2–3), 93–118. https://doi.org/10.1080/19312458.2018.1430754

Mezquita, B., Alfonso‐Arias, C., Martínez‐Jaimez, P., & Borrego, Á. (2024). The use of bibliometrics in nursing science: Topics, data sources and contributions to research and practice. Nursing Open, 11(9), e70036. https://doi.org/10.1002/nop2.70036

Mishra, M. (2025). Comparative analysis of bertopic versus lda: Topic modelling in marketing research. Australasian Marketing Journal, 14413582251399667. https://doi.org/10.1177/14413582251399667

Ogunleye, B., Lancho Barrantes, B. S., & Zakariyyah, K. I. (2025). Topic modelling through the bibliometrics lens and its technique. Artificial Intelligence Review, 58(3), 74. https://doi.org/10.1007/s10462-024-11011-x

Rouhani, S., & Mozaffari, F. (2022). Sentiment analysis researches story narrated by topic modeling approach. Social Sciences & Humanities Open, 6(1), 100309. https://doi.org/10.1016/j.ssaho.2022.100309

Silva, C. C., Galster, M., & Gilson, F. (2021). Topic modeling in software engineering research. Empirical Software Engineering, 26(6), 120. https://doi.org/10.1007/s10664-021-10026-0

Sugeno, Y., & Koizumi, M. (2024). Research trends in public libraries as public spheres in library and information science: Topic modelling with latent dirichlet allocation. Libri, 74(3), 289–304. https://doi.org/10.1515/libri-2024-0041

Thakuria, A., & Deka, D. (2024). A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach. Scientometrics, 129(7), 3841–3869. https://doi.org/10.1007/s11192-024-05058-4

Trivedi, S. K., Chalapathi, D. S., Srivastava, J., Singh, S., & Deb Roy, A. (2026). Insights on emotional labour research: A topic modelling approach. Global Knowledge, Memory and Communication, 75(1–2), 191–216. https://doi.org/10.1108/GKMC-10-2023-0384

Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2020.101582

Wang, C., & Blei, D. M. (2011). Collaborative topic modeling for recommending scientific articles. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 448–456. https://doi.org/10.1145/2020408.2020480

Yang, A. J. (2025). Unraveling topic switching and innovation in science. Information Processing & Management, 62(4), 104171. https://doi.org/10.1016/j.ipm.2025.104171

Abstract

Library and Information Science is among the oldest and intellectually pluralistic scholarly disciplines but the inner thematic structure has not been established. The article develops a systematic and data intensive mapping of the field by the Latent Dirichlet Allocation topic modelling to 7,824 articles in the English language indexed in OpenAlex in the year 2000 to 2025. A ten topic solution is an optimised solution that identifies a group of readable and sensible thematic domains including information literacy digital libraries scientometrics archives and scholarly communication. The analysis of temporal trajectory reveals that there have been consistent expansion at the emergent locations such as scientometrics open access and digital preservation and consolidation at the previously established competency oriented themes. The structural relations and intellectual dependencies that constitute the discipline is also explained through a dynamic topic co occurrence network. At the same time the assessment of the open access allocation and field weighted citation effect is that the scientometrics and health information research have disproportionately high citation effect in terms of its share of corpus. Combined these insights provide a mature and analytically sound explanation of the changing knowledge structure of Library and Information Science with much bearing on the research policy formulation and strategic directions in scholarly communication.

Cover_Letter.docx (6 kB)
Cover Letter

Share

COinS