PurposeThe present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.Design/methodology/approachThe present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.FindingsThe findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”Originality/valueThe originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.
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.
Purpose. Brucellosis is widespread globally and one of the most important zoonotic diseases. Therefore, to fully comprehend the disease and discover ways of prevention and treatment, researchers have conducted some research in this field. Hence, this study will focus on the topic trend of scientific publications of brucellosis. Methods. This study is an applied research using text mining techniques with an analytical approach. The statistical population of the present research is all global publications related to brucellosis. For data extraction, the Scopus citation database was used in the period from 1900 to 2020. The main keywords for search strategy design have been extracted from consultation with thematic specialists and using MESH. Python programming language has been applied to analyze data and implement text mining algorithms. Results. According to results, eight main topics of “Prevention,” “Clinical symptoms,” “Diagnosis,” “Control,” “Treatment,” “Immunology,” “Structural Features,” and “Pathogenicity” have been identified for brucellosis publications. Moreover, the topics “Prevention” and “Pathogenicity” had the highest and lowest prevalence in the field of brucellosis over time, respectively. Conclusion. This study has revealed the topics published in the global publications of brucellosis; the findings can be useful for research centers and universities in determining research priorities in the field of brucellosis.
At the end of 2019, COVID-19 (Coronavirus 2019) emerged in Wuhan, China, and spread rapidly worldwide. The use of virtual social networks, especially Twitter, has increased due to the present condition. The purpose of the present systematic literature review is to review the investigations on Twitter's role in the COVID-19 crisis. For this purpose, an appropriate search strategy was used to extract the studies conducted in the Web of Science and PubMed databases. In the end, 24 articles were reviewed. The results indicate that in the period of the COVID-19 pandemic, the content and tweets posted on Twitter were affected by this crisis, and various people such as the general public, health professionals, and politicians were sharing opinions, emotions, personal experience, and educational content about exposure to COVID-19 on this social media. Therefore, the speed of providing information to people has been one of the main advantages of Twitter during the crisis of COVID-19; however, the risk of using invalid information without scientific citation is also one of the most important concerns of using Twitter among people as well as health and governmental organizations. Thus, users should evaluate information accuracy more carefully and pay attention to the quality and validity of information before employing or sharing it. Governments and professionals can also prevent this disease's contagion even in similar future crises by employing Twitter correctly in the period of crisis and using the useful experience gained from applying social networks in the outbreak of COVID-19.
In late 2019, the coronavirus 2019 (COVID-19) emerged in Wuhan, China, and rapidly spread around the world. Due to this incident, the use of social networks has increased among people. The present narrative review aimed to investigate the studies conducted on the subject of social media and COVID-19 in the Web of Science database. The investigations show that social media has been used to share viewpoints, health care, and distance learning during the COVID-19 crisis. Therefore, using social media can be a valuable means for the governments and experts to prevent the spread of this epidemic and even in similar future crises.
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