قارن الطرق

	VOSviewer و CiteSpace: أدوات التحليل الببليومتري والتصوير البياني	تحليل تكرار الكلمات المفتاحية المشترك
المجال	القياسات الببليومترية	القياسات الببليومترية
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2006–2010	2000s
صاحب الطريقة	Nees Jan van Eck & Ludo Waltman (VOSviewer); Chaomei Chen (CiteSpace)	Bibliometric research community
النوع	Tool	Method
المصدر التأسيسي	Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.	Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166.
الأسماء البديلة	bibliometric mapping software, citation visualization tools, science mapping tools	term co-occurrence, keyword network analysis, thematic analysis, term clustering
ذات صلة	4	4
الملخص	VOSviewer and CiteSpace are specialized software tools designed to conduct bibliometric analysis and create science maps from research literature. VOSviewer (developed by Van Eck & Waltman, 2010) excels at creating publication landscapes through co-occurrence, co-citation, and bibliographic coupling analysis with intuitive visual output. CiteSpace (developed by Chaomei Chen, 2006) focuses on detecting emerging research trends and research fronts through direct citation analysis and specialized temporal algorithms. Together, these tools democratized science mapping, enabling researchers without programming expertise to visualize research domains comprehensively.	Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries.