Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv

Authors

  • Nishad Deshpande CSIR-Unit for Research and Development of Information Products, Pune, Maharashtra, India
  • Virendra Ligade Department of Pharmacy Management, Manipal College of Pharmaceutical Science, MAHE, Manipal, Karnataka, India
  • Shabib-Ahmed Shaikh CSIR-Unit for Research and Development of Information Products, Pune, Maharashtra, India
  • Alok Khode dCSIR-Unit for Research and Development of Information Products, Pune, Maharashtra, India

DOI:

https://doi.org/10.56042/alis.v70i1.71939

Keywords:

COVID-19, Vaccine, Preprints, LDA, Topic modelling

Abstract

Two thousand one hundred and ninety-eight research publications on COVID-19 vaccines in MedRxiv preprint
repository during January 01, 2020 and December 31, 2021 were analyzed for topic modelling with unsupervised inference
method. Latent Dirichlet Allocation (LDA) method was used to investigate the thematic structure of the preprints. It was
observed that the published articles were related to either clinical trials or patient responses to vaccine or modelling for
various applications such as infection transmission, vaccine allocation, vaccine hesitancy etc.

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Published

2023-04-21

Issue

Vol. 70 No. 1 (2023): Annals of Library and Information Studies

Section

Articles

How to Cite

Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv. (2023). Annals of Library and Information Studies , 70(1), 41-51. https://doi.org/10.56042/alis.v70i1.71939

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