Premium
A lead‐lag analysis of the topic evolution patterns for preprints and publications
Author(s) -
Hu Beibei,
Dong Xianlei,
Zhang Chenwei,
Bowman Timothy D.,
Ding Ying,
Milojević Staša,
Ni Chaoqun,
Yan Erjia,
Larivière Vincent
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23347
Subject(s) - lag , lead (geology) , computer science , time lag , data science , information retrieval , biology , paleontology , computer network
This study applied LDA (latent D irichlet allocation) and regression analysis to conduct a lead‐lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the W eb of S cience ( WoS ) in astrophysics over the last 20 years (1992–2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv . This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.