year 17, Issue 2 (March - April 2023)                   Iran J Med Microbiol 2023, 17(2): 150-160 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Danesh F, Rahimi F. Mining of Emerging trends of Covid-19 thematic areas in National and International publications. Iran J Med Microbiol 2023; 17 (2) :150-160
URL: http://ijmm.ir/article-1-1988-en.html
1- Information Management Department, Regional Information Center for Science and Technology (RICeST), Shiraz, Iran
2- Information Management Department, Regional Information Center for Science and Technology (RICeST), Shiraz, Iran , frahimi@ricest.ac.ir
Abstract:   (2137 Views)

Background and Aim: The results from the analysis of COVID-19 literature by employing text-mining techniques are of particular importance for researchers, policymakers, and planners of medical sciences at the national and international levels, avoiding parallel research and waste of time and budget. The paper explore emerging topics and the trend of scientific words at the national and international levels in the subject area of COVID-19.
Materials and Methods: This applied research was conducted by employing the text-mining and its related algorithms and classifying texts. The population consists of all COVID-19 articles indexed in PubMed Central® (PMC). The number of records retrieved was 160,862 items until June 10, 2021. Among these, 3143 national and 157,719 international COVID-19 articles. Python and its related libraries were applied. The most significant words were also identified and reported based on TF-IDF weighting. Emerging topics were identified according to the weighted average growth.
Results: "COVID", "infect", and "cell" were among the most important words used in international COVID-19 articles. In addition, the most important words in the national COVID-19 articles were "patient", "SARS-Cov", and "COVID".
Conclusion: Among the most important conclusions that can be inferred from the trend of word change used in the COVID-19 literature is that the most significant words in international literature differ significantly from those in national literature, as international research focuses on COVID-19 and the infections caused by it. In contrast, national research focuses on COVID-19 and patients. Another significant result is the annual word-changing national and international literature.

Full-Text [PDF 2059 kb]   (514 Downloads) |   |   Full-Text (HTML)  (558 Views)  
Type of Study: Original Research Article | Subject: Scientometric Analysis
Received: 2022/12/13 | Accepted: 2023/02/28 | ePublished: 2023/03/30

References
1. Cui X, Wang P, Wei Z. Emergency use of COVID-19 vaccines recommended by the World Health Organization (WHO) as of June 2021. Drug Discov Ther. 2021;15(4):222-4. [DOI:10.5582/ddt.2021.01064] [PMID]
2. Saeidnia H, Mohammadzadeh Z, Saeidnia M, Mahmoodzadeh A, Ghorbani N, Hasanzadeh M. Identifying Requirements of a Self-care System on Smartphones for Preventing Coronavirus Disease 2019 (COVID-19). Iran J Med Microbiol. 2020;14(3):241-51. [DOI:10.30699/ijmm.14.3.241]
3. Danesh F, Ghavidel S. Coronavirus: Scientometrics of 50 Years of Global Scientific Productions. Iran J Med Microbiol. 2020;14(1):1-16. [DOI:10.30699/ijmm.14.1.1]
4. Shehata A, El Dakar M, Salem N, editors. Top COVID-19 100 vaccine papers: An Altmetric study. 2021 22nd International Arab Conference on Information Technology (ACIT); 2021. [DOI:10.1109/ACIT53391.2021.9677241] [PMID] [PMCID]
5. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J. 2008;22(2):338-42. [DOI:10.1096/fj.07-9492LSF] [PMID]
6. Cheng X, Shuai C, Liu J, Wang J, Liu Y, Li W, et al. Topic modelling of ecology, environment and poverty nexus: An integrated framework. Agric Ecosyst Environ. 2018;267:1-14. [DOI:10.1016/j.agee.2018.07.022]
7. Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78(11):15169-211. [DOI:10.1007/s11042-018-6894-4]
8. O'Callaghan D, Greene D, Carthy J, Cunningham P. An analysis of the coherence of descriptors in topic modeling. Expert Syst Appl. 2015;42(13):5645-57. [DOI:10.1016/j.eswa.2015.02.055]
9. Steyvers M, Griffiths T. Probabilistic topic models. Handbook of latent semantic analysis. 15 ed: Psychology Press; 2007. p. 439-60.
10. Meskarpour Amiri M, Nasiri T, Mehdizadeh P. Subjects clustering analysis and science mapping on COVID-19 researches in scopus database. J Mil Med. 2020;22(6):663-9.
11. Tran BX, Ha GH, Nguyen LH, Vu GT, Hoang MT, Le HT, et al. Studies of novel coronavirus disease 19 (COVID-19) pandemic: a global analysis of literature. Int J Environ Res Public Health. 2020;17(11):4095. [DOI:10.3390/ijerph17114095] [PMID] [PMCID]
12. Cheng X, Cao Q, Liao SS. An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation. J Inf Sci. 2022;48(3):304-20. [DOI:10.1177/0165551520954674] [PMCID]
13. Doanvo A, Qian X, Ramjee D, Piontkivska H, Desai A, Majumder M. Machine Learning Maps Research Needs in COVID-19 Literature. Patterns. 2020;1(9):100123. [DOI:10.1016/j.patter.2020.100123] [PMID] [PMCID]
14. Han X, Wang J, Zhang M, Wang X. Using social media to mine and analyze public opinion related to COVID-19 in China. Int J Environ Res Public Health. 2020;17(8):2788. [DOI:10.3390/ijerph17082788] [PMID] [PMCID]
15. Chire Saire J, Pineda-Briseño A. Text Mining for Covid-19 Analysis in Latin America. Artif Intell. 2021:257-94. [DOI:10.1007/978-3-030-69744-0_16]
16. Abdeen MAR, Hamed AA, Wu X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Appl Sci. 2021;11(16):7265. [DOI:10.3390/app11167265]
17. Jafari Baghiabadi S, Razieh F. Studying of Research Related to COVID-19 Vaccine in Iran and the World: A Thematic Analysis and Scientific Collaborations. Iran J Med Microbiol. 2021;15(4):414-57. [DOI:10.30699/ijmm.15.4.414]
18. Danesh F, Dastani M, Ghorbani M. Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis. Libr Hi Tech. 2021;39(3):855-72. [DOI:10.1108/LHT-09-2020-0216]
19. Žižka J, Dařena F, Svoboda A. Text mining with machine learning: principles and techniques: Crc Press; 2019. [DOI:10.1201/9780429469275]
20. Rehurek R, Sojka P. Software framework for topic modelling with large corpora. Proceedings of the LREC workshop on new challenges for NLP frameworks: Citeseer; 2010.
21. Kim YM, Delen D. Medical informatics research trend analysis: A text mining approach. Health Informatics J. 2018;24(4):432-52. [DOI:10.1177/1460458216678443] [PMID]
22. Malaterre C, Lareau F, Pulizzotto D, St-Onge J. Eight journals over eight decades: a computational topic-modeling approach to contemporary philosophy of science. Synthese. 2021;199(1):2883-923. [DOI:10.1007/s11229-020-02915-6]
23. Danesh F, Dastani M. Application of Artificial Intelligence for Discovering the Subject Classes of National Publications Based on International Publications: COVID-19 Publications. Digit Health. under review; 2023.
24. Dornick C, Kumar A. Seidenberger S, Seidle E, Mukherjee P. Analysis of Patterns and Trends in COVID-19 Research. Procedia Comput Sci; 2021 [DOI:10.1016/j.procs.2021.05.032]
25. Haghani M, Bliemer MCJ, Goerlandt F, Li J. The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review. Saf Sci. 2020;129:104806. [DOI:10.1016/j.ssci.2020.104806] [PMID] [PMCID]
26. Gupta A, Aeron S, Agrawal A, Gupta H. Trends in COVID-19 publications: streamlining research using NLP and LDA. Front digit health. 2021;3:686720. [DOI:10.3389/fdgth.2021.686720] [PMID] [PMCID]
27. Hossain MM. Current status of global research on novel coronavirus disease (Covid-19): A bibliometric analysis and knowledge mapping. F1000Research. 2020(9):374. [DOI:10.12688/f1000research.23690.1]
28. Mullard A. COVID-19 vaccine development pipeline gears up. Lancet. 2020;395(10239):1751-2. [DOI:10.1016/S0140-6736(20)31252-6] [PMID]
29. Farahati M. Psychological impacts of the spread of coronavirus in society. Soc impact assess. 2020(2):207-5.
30. Haleem A, Javaid M, Vaishya R, Deshmukh SG. Areas of academic research with the impact of COVID-19. Am J Emerg Med. 2020;38(7):1524-6. [DOI:10.1016/j.ajem.2020.04.022] [PMID] [PMCID]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Iranian Journal of Medical Microbiology

Designed & Developed by : Yektaweb | Publisher: Farname Inc