Co-Occurrence Analysis of COVID-19 Publications with an Emphasis on the Global Health Governance (GHG).

Background: Analyzing co-occurrence is an effective way to monitor the overview of topic spreading. The present study aimed to conduct a co-occurrence analysis of scientific publications related to COVID-19, emphasizing Global Health Governance (GHG). Materials and Methods: This applied research with an analytical approach was carried out on all the scientific publications related to COVID-19, emphasizing GHG (51056 records), extracted from PubMed Central on 26/01/2022. The research population consisted of all the scientific publications related to COVID-19, emphasizing GHG (51056 records), extracted from PubMed Central on 26/01/2022. The data were analyzed using BibExcel, UCINET, Excel, and SPSS software, and Spearman's test was used to confirm correlations. Results: The co-word network of the thematic area of COVID-19 includes 226 nodes and 7292 edges. COVID-19 and the pandemic formed the most co-word pairs with 2224 connections. The COVID-19* mental health and COVID-19* anxiety, with 1019 and 925 connections, are ranked next, respectively. The term COVID-19 is ranked first with a centrality index of 225. The keywords of pandemic and public health are ranked second and third with the centrality index of 217 and 206, respectively. Conclusion: The global approach of studies related to COVID-19 is more inclined to the epidemiological and public health fields. Assuming the GHG, detailed and comprehensive planning should be performed to strengthen these studies and pave the way for international cooperation, determining research requisites, and developing applied research studies.

Text classification technique for discovering country-based publications from international COVID-19 publications.

Objective: The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques. Methods: The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics. Results: The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of "Social and Technology in COVID-19" at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively. Conclusion: One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category "Covid-19 Proteins: Vaccine and Antibody Response," Iranian publications have a common publishing and research trend with international ones.

Evolution of retracted publications in the medical sciences: Citations analysis, bibliometrics, and altmetrics trends.

We investigated reasons for retraction, pre-and post-retraction citations and Altmetrics indicators of retracted publications in the medical sciences from 2016 to 2020. Data were retrieved from Scopus (n = 840). The Retraction Watch database was used to identify the reasons for retraction and the time that elapsed from publication to retraction. The findings showed that intentional errors were the most prevalent reasons for retraction. China (438), the United States (130), and India (51) have the largest share of retractions. These retracted publications were cited 5,659 times in other research publications, of which 1,559 citations occurred after the retraction, which should raise concern. These retracted papers were also shared in online platforms, mainly on Twitter and by members of the general public. We recommend that the early detection of retracted papers may help to reduce the rate of citation and sharing of these publications, and minimize their negative impact.