Chen CM "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature " JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 57 (3): 359-377 FEB 1 2006

[Eugene Garfield]

Chaomei Chen :   E-mail Addresses: chaomei.chen at cis.drexel.edu


Title:     CiteSpace II: Detecting and visualizing emerging trends and
transient patterns in scientific literature

Author(s): Chen CM

Source: JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND
TECHNOLOGY 57 (3): 359-377 FEB 1 2006

Document Type: Article    Language: English
Cited References: 61      Times Cited: 0

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.

Addresses: Chen CM (reprint author), Drexel Univ, Coll Informat Sci &
Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA
Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA

E-mail Addresses: chaomei.chen at cis.drexel.edu

Publisher: JOHN WILEY & SONS INC, 111 RIVER ST, HOBOKEN, NJ 07030 USA
Subject Category: COMPUTER SCIENCE, INFORMATION SYSTEMS; INFORMATION
SCIENCE & LIBRARY SCIENCE
IDS Number: 006YA

ISSN: 1532-2882

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