CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature
C. Chen. Journal of the American Society for Information Science and Technology, 57 (3):
359--377(Februar 2006)
DOI: 10.1002/asi.20317
Zusammenfassung
This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature—an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
%0 Journal Article
%1 chen_citespace_2006
%A Chen, Chaomei
%D 2006
%J Journal of the American Society for Information Science and Technology
%K szientometrie
%N 3
%P 359--377
%R 10.1002/asi.20317
%T CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature
%U http://onlinelibrary.wiley.com/doi/10.1002/asi.20317/abstract
%V 57
%X This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature—an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
@article{chen_citespace_2006,
abstract = {This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature—an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.},
added-at = {2018-11-04T17:00:37.000+0100},
author = {Chen, Chaomei},
biburl = {https://www.bibsonomy.org/bibtex/2daf6669772c41df598bf29475effb37d/lepsky},
doi = {10.1002/asi.20317},
interhash = {7c93cd7c2d93ec5a09091a42f875143c},
intrahash = {daf6669772c41df598bf29475effb37d},
issn = {1532-2890},
journal = {Journal of the American Society for Information Science and Technology},
keywords = {szientometrie},
language = {en},
month = feb,
number = 3,
pages = {359--377},
shorttitle = {{CiteSpace} {II}},
timestamp = {2018-11-04T17:00:37.000+0100},
title = {{CiteSpace} {II} : detecting and visualizing emerging trends and transient patterns in scientific literature},
url = {http://onlinelibrary.wiley.com/doi/10.1002/asi.20317/abstract},
urldate = {2017-01-27},
volume = 57,
year = 2006
}