Modeling citation dynamics of “atypical” articles

Zhongyang He, Zhen Lei, Dashun Wang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1148-1160
Number of pages13
JournalJournal of the Association for Information Science and Technology
Volume69
Issue number9
DOIs
StatePublished - Sep 1 2018

Fingerprint

event
science
Trajectories
Citations
Modeling
time
Factors
Trajectory

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

Cite this

@article{1ce5be904fdd4bf9b709600a8f6f83f8,
title = "Modeling citation dynamics of “atypical” articles",
abstract = "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.",
author = "Zhongyang He and Zhen Lei and Dashun Wang",
year = "2018",
month = "9",
day = "1",
doi = "10.1002/asi.24041",
language = "English (US)",
volume = "69",
pages = "1148--1160",
journal = "Journal of the Association for Information Science and Technology",
issn = "2330-1635",
publisher = "John Wiley and Sons Ltd",
number = "9",

}

Modeling citation dynamics of “atypical” articles. / He, Zhongyang; Lei, Zhen; Wang, Dashun.

In: Journal of the Association for Information Science and Technology, Vol. 69, No. 9, 01.09.2018, p. 1148-1160.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modeling citation dynamics of “atypical” articles

AU - He, Zhongyang

AU - Lei, Zhen

AU - Wang, Dashun

PY - 2018/9/1

Y1 - 2018/9/1

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.

UR - http://www.scopus.com/inward/record.url?scp=85046551554&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046551554&partnerID=8YFLogxK

U2 - 10.1002/asi.24041

DO - 10.1002/asi.24041

M3 - Article

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

SN - 2330-1635

IS - 9

ER -