Knowledge Maps in Web Science

Clare Hooper, Isabella Peters, Cécile Robin


This paper describes a mixed methods analysis of papers published at the ACM Web Science Conference series from 2009 to 2016, using co-citation analysis, bibliographic coupling, natural language processing, topic modelling and network visualisation techniques. The knowledge base of the Web Science community and the knowledge transfer from the ACM Web Science Conference series are studied, revealing major themes and key authors as a map of Web Science. In particular, the foundations of the Web Science community are revealed via co-citation analysis of authors of papers cited by ACM Web Science papers, while NLP analysis reveals topical descriptors and application contexts of Web Science. Finally, author-based bibliographic coupling of papers published at Web Science reveals authors who have been influenced by the Web Science community. In sum, this paper presents a knowledge map of the Web Science discipline visualizing topical foci, methodical roots in various disciplines, and key players in Web Science research.

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