Bibliometric and Text Mining Analysis on COVID-19 Research Projects in Iran

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Extended Abstract
Background and Objectives
In response to the COVID-19 epidemic, researchers around the world have conducted various studies on different dimensions of the disease. The researchers and various research teams have designed and conducted an extensive range of studies related to COVID-19 including epidemiology, disease surveillance, consequences of the disease, and clinical trials. In Iran, different ongoing or completed research projects have also been approved to identify various aspects of the disease. Research project proposals are documents prepared by researchers to carry out research projects and are formulated according to particular demands for research in the society on various subjects such as disease burden, epidemics, threats and natural factors. Therefore, this study investigated the structure and topics of COVID-19 research projects approved in Iran.
Material and Methods
This applied research, adopting an analytical approach, was conducted using bibliometric and text mining methods. The statistical population was the COVID-19 research projects approved in Iran in 2020. These research projects were extracted from the database of Iran national committee for ethics in biomedical research (ethics.research.ac.ir). To identify the topics of the research projects on COVID-19 by their English titles, topic modeling algorithms were used in the Python programming language.This database, includes the bibliographic data of all research projects which have been approved by the medical sciences, received implementation ethics license. The projects related to medical sciences are registered in this database to receive an ethics code before implementation; thus, all Iranian projects within COVID-19 topical area are registered in this database before being implemented, and their bibliographic data can be retrieved and accessed. After extracting data related to the approved COVID-19 research projects from the mentioned database, a topical modeling algorithm named Latent Dirichlet Allocation (LDA) was employed to identify the topics of research projects by the English titles of COVID-19 approved research projects in Iran, using text mining techniques. Text mining process employed in this study includes three stages; (1) data preprocessing (2) implementation of text mining and visualization techniques, and (3) the analysis of results and knowledge extraction. In the present investigation, Python programming language and its libraries related to text mining, such as Gensim, NLTK, and Spacy, were used to implement text mining algorithms.
Results
We selected a total of 6641 COVID-19 research projects approved and conducted in 93 different Iranian universities and research centers. The main bulk of the research in this area had been conducted by Tehran University of Medical Sciences, Shahid Beheshti University of Medical Sciences, and Shiraz University of Medical Sciences including 687,662 and 351 cases respectively. The highest number of research projects approved in Iran included 1238, 796, 796 cases in April, May, and March, respectively. The results also revealed that the researchers to contribute most, as the main executor, to the approved research projects included Amir Vahedian Azimi from Baqiyatallah University of Medical Sciences, Jamshid Yazdani Charati from Mazandaran University of Medical Sciences, Ramin Sami from Isfahan University of Medical Sciences and Hossein Sheybani from Shahroud University of Medical Sciences each of whom had had 12 research projects approved. The results of text mining techniques also indicated that the terms COVID, patients, evaluation, hospital, and disease were among the most frequent words used in the titles of COVID-19 research projects approved in Iran. The results obtained from topical modeling have identified 12 distinct themes for the research projects in this area, including treatment, care needs of medical staff, factors of disease severity, mental health and preventive behavior, diagnostic and laboratory studies, Immunology studies, vitamins and minerals, cardiovascular diseases, vaccine studies, job and life stress, experiences of nurses, patients and their families and prevalence and symptoms.
Conclusion
In the present study, bibliometric and text mining techniques were applied to identify the topical structure of COVID-19 research projects approved in Iran. The results of this study clearly depicted the structural and topic status of research COVID-19 projects approved in Iran during the COVID-19 epidemic. The results seem to be useful for planners and policy-makers in research and medical organizations to identify topics that are understudied by researchers and also to formulate new research priorities and requirements in this field.
Practical Implications of Research
This research has used bibliometric and text mining techniques to identify the thematic structure of research projects approved by COVID-19 in Iran. The results of this study can be useful for planners and policy makers in research and medical organizations in order to identify topics that are less frequently considered by researchers and also to formulate new research priorities and requirements in this field.
Ethical Considerations
The present study was extracted from a research project approved by the Vice Chancellor for Research and Technology of Gonabad University of Medical Sciences with the code A-10-1263-5.
Conflict of Interest
The authors state that there is no conflict of interest in the present study.
Acknowledgment
Researchers express their gratitude to the Vice Chancellor for Research, Technology and Infectious Diseases Research Center of Gonabad University of Medical Sciences for their financial and spiritual support of this research.