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 language | English (US) |
|---|---|
| Pages (from-to) | 1148-1160 |
| Number of pages | 13 |
| Journal | Journal of the Association for Information Science and Technology |
| Volume | 69 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2018 |
Funding
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.
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Library and Information Sciences