Document Type : Original Article
Authors
1 Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Assistant Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 MSc, Knowledge and Information Science, Shahid Chamran University of Ahvaz AND School of Rehabilitation Sciences, Jundishapur University of Medical Sciences, Ahvaz, Iran
Abstract
Introduction: Cohesion indicator is one of the scientific mapping tools which uses the most important words in documents to study the conceptual structure of a research area. The purpose of the present study was to analyze the structure of the scientific map of autism outputs through lexical co-occurrence analysis in the Clarivate Analytics Web of Science Database.Methods: This study was conducted using scientometric method. The research population consisted of 14186 autism-related records published between the years 2010 and 2017 at the Clarivate Analytics Web of Science Database. The data were analyzed using social network analysis method.Results: The words “ability, malformations, syndrome, disorder, phenotype, and neurons” were the main vocabulary in the domain of autism spectrum disorder. These words also received the highest score in terms of centrality factors. Furthermore, in terms of macro-indicators, the domain of autism was coherent. In this area, the United States, the United Kingdom, and Canada had produced more records compared to other countries. The universities of California, London, and Harvard had also been the most productive universities in the international arena. Among Iranian universities, Tehran University of Medical Sciences, Islamic Azad University, and Shahid Beheshti University of Medical Sciences had more publications compared to other universities. Among the top researchers in terms of number of international productions "Zwaigenbaum L.", "Matson JL." and "Gillberg C." and among Iranian researchers "Memari A", "Mashayedi P", and "Ahmadloo M" had the best works.Conclusion: The information extracted from lexical co-occurrence map can help to improve policy-making in scientific fields. In this map, each word or group of words represents a particular area. Therefore, these maps can be used to make efficient decisions regarding resource allocation and distribution. Furthermore, these maps can help researchers get acquainted with new topics and top researchers in each field.
Keywords
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