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

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Title Statement <strong>Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv</strong>
 
Added Entry - Uncontrolled Name Deshpande, Nishad ; CSIR Unit for Research and Development of Information Products, Pune, Maharashtra, India
Ligade, Virendra ; Department of Pharmacy Management Manipal College of Pharmaceutical Sciences MAHE,Manipal,India.
Shaikh, Shabib-Ahmed ; CSIR Unit for Research and Development of Information Products, Pune, Maharashtra, India
Khode, Alok ; CSIR Unit for Research and Development of Information Products, Pune, Maharashtra, India
 
Uncontrolled Index Term Information Science, Computer Applications
COVID-19, Vaccine, Preprints, LDA, Topic modelling
 
Summary, etc. Two thousand one hundred and ninety-eight research publications on COVID-19 vaccines in MedRxiv preprintrepository during January 01, 2020 and December 31, 2021 were analyzed for topic modelling with unsupervised inferencemethod. Latent Dirichlet Allocation (LDA) method was used to investigate the thematic structure of the preprints. It wasobserved that the published articles were related to either clinical trials or patient responses to vaccine or modelling forvarious applications such as infection transmission, vaccine allocation, vaccine hesitancy etc.
 
Publication, Distribution, Etc. Annals of Library and Information Studies (ALIS)
2023-04-21 11:08:25
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/ALIS/article/view/71939
 
Data Source Entry Annals of Library and Information Studies (ALIS); ##issue.vol## 70, ##issue.no## 1 (2023): Annals of Library and Information Studies
 
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