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    BIBLIOMETRIC ANALYSIS OF PUBLICATIONS RELATED TO ARTIFICIAL INTELLIGENCE AND ITS COMPONENTS IN THE COVID-19 PERIOD
    (Dünya Kongre, 2022) Karayel, Tuğçe; Kurutkan, Mehmet Nurullah
    Purpose: The main purpose of this study is to conduct a bibliometric analysis of publications in the field of Covid-19 and artificial intelligence. The performance of the field, its conceptual and social structure, the thematic development map and the identification of its main clusters serve this purpose. Method: In this article, R-based Bibliometrix, VOSviwer, SciMAT and Citespace software were used. Web of Science articles dec dec 2020-2021 have been downloaded as raw data from the Core collection with the search staretit. In total, 1367 articles were studied. Conceptual and social structure analyses were carried out from information structures with performance analyses. The process has been completed with the analysis of engine themes and main clusters. Finding: From the point of view of conceptual structure analyses, it was determined that the studies were analyzed under the headings classification, diagnosis and treatment. According to the results of the social structure, the USA, China, India, Italy and the UK are both the most broadcasting countries and the countries that are most open to dec-country cooperation. According to thematic diagram analysis, themes based on artificial intelligence tools and algorithms used in “Transfer- Learning and Support Vector Machines”, covid 19 disease diagnosis, social media, mental health and covid process have come to the fore. Result: The results of the bibliometric analysis provided information about the quality of published studies on COVID-19 and artificial intelligence, as well as research areas. In particular, artificial intelligence applications based on “Transfer- Learning” and “Support Vector Machines”, forecasting and social media data have the potential to become popular research topics.