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 2018

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All Science Journal Classification (ASJC) codes

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

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