TY - JOUR
T1 - Modeling citation dynamics of “atypical” articles
AU - He, Zhongyang
AU - Lei, Zhen
AU - Wang, Dashun
N1 - Funding Information:
This work is supported by the Air Force Office of Scientific Research under award number FA9550-15-1-0162 and FA9550-17-1-0089, Northwestern University’s Data Science Initiative, and U.S. National Science Foundation Grant SMA-1360205. We thank the reviewers for their insightful comments that greatly improved our work.
Publisher Copyright:
© 2018 ASIS&T
PY - 2018/9
Y1 - 2018/9
N2 - Modeling and predicting citation dynamics of individual articles is important due to its critical role in a wide range of decisions in science. While the current modeling framework successfully captures citation dynamics of typical articles, there exists a nonnegligible, and perhaps most interesting, fraction of atypical articles whose citation trajectories do not follow the normal rise-and-fall pattern. Here we systematically study and classify citation patterns of atypical articles, finding that they can be characterized by awakened articles, second-acts, and a combination of both. We propose a second-act model that can accurately describe the citation dynamics of second-act articles. The model not only provides a mechanistic framework to understand citation patterns of atypical articles, separating factors that drive impact, but it also offers new capabilities to identify the time of exogenous events that influence citations.
AB - Modeling and predicting citation dynamics of individual articles is important due to its critical role in a wide range of decisions in science. While the current modeling framework successfully captures citation dynamics of typical articles, there exists a nonnegligible, and perhaps most interesting, fraction of atypical articles whose citation trajectories do not follow the normal rise-and-fall pattern. Here we systematically study and classify citation patterns of atypical articles, finding that they can be characterized by awakened articles, second-acts, and a combination of both. We propose a second-act model that can accurately describe the citation dynamics of second-act articles. The model not only provides a mechanistic framework to understand citation patterns of atypical articles, separating factors that drive impact, but it also offers new capabilities to identify the time of exogenous events that influence citations.
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U2 - 10.1002/asi.24041
DO - 10.1002/asi.24041
M3 - Article
AN - SCOPUS:85046551554
SN - 2330-1635
VL - 69
SP - 1148
EP - 1160
JO - Journal of the Association for Information Science and Technology
JF - Journal of the Association for Information Science and Technology
IS - 9
ER -