Social media-based sleeping beauties: Defining, identifying and features
Jianhua Hou and
Xiucai Yang
Journal of Informetrics, 2020, vol. 14, issue 2
Abstract:
The sleeping beauties in science signify a unique knowledge diffusion trajectory created by citation after the publication of scientific literature. However, in social media, scientific knowledge creates a new diffusion trajectory through social media metrics such as View, Save, Discussed, and Recommendations. This study aims to define social media–based sleeping beauties S-SB in science by using social media metrics which we termed as citation-based Sleeping Beauties in science, C-SB. We constructed a quantitative method to identify S-SB and conducted an empirical study of all types of 4019 articles published in PLOS Biology. Comparison of the S-SB and C-SB results revealed that from the perspective of social media metrics, C-SB has become the literature of S-total elements early gradual awakening type, S-total elements delay gradual awakening type, and S-early sudden awakening type. Moreover, the awakening time of C-SB literature under the action of social media metrics was found to be 4–5 years earlier than that under the action of citation-based indicators. Both C-SB and S-SB included significant “Editorial Material,” establishing that “Editorial Material” type literature is noteworthy while promoting the diffusion of scientific knowledge. Overall, this study extends the perspective of sleeping beauties in science.
Keywords: Social media–based sleeping beauties; Citation-based sleeping beauties; Identification; Awakening mode (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157719303049
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:14:y:2020:i:2:s1751157719303049
DOI: 10.1016/j.joi.2020.101012
Access Statistics for this article
Journal of Informetrics is currently edited by Leo Egghe
More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().