Citation burst prediction in a bibliometric network.

Autor/inn/en	Amjad, Tehmina; Shahid, Nafeesa; Daud, Ali; Khatoon, Asma
Titel	Citation burst prediction in a bibliometric network.
Quelle	In: Scientometrics, (2022) 5, S.2773-2790 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-022-04344-3
Schlagwörter	Journal; Conference; Citation burst; Citations analysis; Features; Correlation; Multiple linear regression
Abstract	Abstract In the field of computer science, both journal and conference publications are considered valuable. The popularity of an author is mostly determined by the paper’s high citations in a short time. Features that can help to attract higher visibility are not yet thoroughly investigated in the literature. This study aims to investigate the impact of the several features on received citations, for articles published in both journals or conferences. The correlation analysis and multiple linear regression models are applied to explore the strength of all related features. The study helps in finding the impact of the individual features on the number of citations both for journals and conferences, and to predict future citations. AMiner citation dataset has been used for experimental analysis. The findings of the study show that in the case of journal publications, “author first-year citations” and “author total citation” are the most important features. While, in the case of conference publications, “author total citation” is more effective as compared to other features. In the case of journal publications, the multiple linear regression model shows the coefficient of determination (R2) is 0.975 and accuracy 0.846. For the conference publications, the R2 value and accuracy are 0.877 and 0.846, respectively.
Erfasst von	OLC
Update	2023/2/05