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Topic diversity: A discipline scheme‐free diversity measurement for journals
Author(s) -
Bu Yi,
Li Mengyang,
Gu Weiye,
Huang Winbin
Publication year - 2021
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24433
Subject(s) - scientometrics , diversity (politics) , granularity , citation , computer science , data science , bibliometrics , information retrieval , library science , sociology , anthropology , operating system
Scientometrics has many citation‐based measurements for characterizing diversity, but most of these measurements depend on human‐designed categories and the granularity of discipline classifications sometimes does not allow in‐depth analysis. As such, the current paper proposes a new measurement for quantifying journals' diversity by utilizing the abstracts of scientific publications in journals, namely topic diversity ( TD ). Specifically, we apply a topic detection method to extract fine‐grained topics, rather than disciplines, in journals and adapt certain diversity indicators to calculate TD . Since TD only needs as inputs abstracts of publications rather than citing relationships between publications, this measurement has the potential to be widely used in scientometrics.