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{"title":"Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv","authors":"N. Deshpande, V. Ligade, S. Shaikh, A. Khode","doi":"10.56042/alis.v70i1.71939","DOIUrl":null,"url":null,"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. © 2023, National Institute of Science Communication and Policy Research. All rights reserved.","PeriodicalId":42973,"journal":{"name":"Annals of Library and Information Studies","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Library and Information Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56042/alis.v70i1.71939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
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预印本服务器MedRxiv上发表的COVID-19疫苗文章的主题建模分析
分析2020年1月1日至2021年12月31日MedRxiv预印本库中关于COVID-19疫苗的2898篇研究论文，采用无监督推理方法进行主题建模。采用潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)方法对预印本的主题结构进行研究。据观察，发表的文章要么与临床试验有关，要么与患者对疫苗的反应有关，要么与感染传播、疫苗分配、疫苗犹豫等各种应用的建模有关。©2023，国家科学传播和政策研究所。版权所有。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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