Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Abstract

COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.

A topic trend analysis on COVID-19 literature

“…Previous studies 6 , 10 , 12 – 14 performed topic models using a concatenated string of titles, keywords, and abstracts of COVID-19 publications, which can be redundant and produce a bias in calculating word frequency. LDA does not allow the inclusion of information other than text, so the temporal evolution of the topics presented by Alga et al 10 and Colavizza et al 6 is just qualitative.…”

“…In Table 2, the main related works are reported. Table 2 shows that LDA 47 was the most used method 6,10,13,14,48 to analyze text and classify publication. STM, the method for topic modeling we used, was applied only by Ebadi et al 12 who considered PubMed and ArXiv literature and included the month of publication to estimate the prevalence of topics using a linear relationship.…”

“…6,8 These investigations within the Coronavirus research allowed more detailed analysis of the content and the publications' characteristics. Earlier works 6,[8][9][10][11][12][13][14][15] are limited to a narrow range of time and do not consider the evolution over time of literature. To get a clear picture, it is necessary to reconstruct the timeline of COVID-19.…”

“…24 The problem we address in this work is the rapid growth of literature since (1) quality of publications is not guaranteed, 3,25,26 (2) the search about a specific topic is time expensive and the results include noisy data, 4 and (3) repositories on COVID-19 literature built so far still lack available data. 5,7 Previous studies 6,[8][9][10][11][12][13][14][15] tried to give an overview about the COVID-19 literature, but they were mainly published in 2020 and cannot therefore include later data. Furthermore, the approaches used aimed to describe the data instead of modeling them.…”

LDA-based topic modeling for COVID-19-related sports research trends

“…The exponential publishing of COVID-19-related studies in a short period includes studies that look at the impacts of COVID-19 on sports. Extensive research is being conducted on various aspects of the relationship between COVID-19 and sports( Älgå et al, 2020 ; Dastani and Danesh, 2021 ; Zhu and Lei, 2022 ). Scientists and researchers around the world have been conducting significant research on aspects of and methods for investigating, problem-solving, and planning related to COVID-19, the results of which have been published in peer-reviewed journals ( Kemp et al, 2021 ; Teare and Taks, 2021 ).…”