An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

Xi Zhang (), Xianhai Wang, Hongke Zhao (), Patricia Ordóñez de Pablos, Yongqiang Sun and Hui Xiong
Additional contact information
Xi Zhang: Tianjin University
Xianhai Wang: Tianjin University
Hongke Zhao: Tianjin University
Patricia Ordóñez de Pablos: University of Oviedo
Yongqiang Sun: Wuhan University
Hui Xiong: Rutgers University

Scientometrics, 2019, vol. 119, issue 3, No 2, 1344 pages

Abstract: Abstract Altmetrics indices are increasingly applied to measure scholarly influence in recent years because they can reflect the influence of research outputs more timely comparing with traditional measurements. Simultaneously, artificial intelligence (AI), as an emerging interdiscipline, has a rapid development in these years. Traditional indices can’t reflect the influence of the AI research outputs quickly, thus more timely altmetrics indices are needed. In this paper, we conduct four studies about altmetrics indices and AI research outputs based on the datasets collected from Altmetric.com and Scopus database. First, we provide a review of the research status in the AI field. Second, we show the AI researches that attracted the most attention. Third, we demonstrate the general effectiveness of altmetrics indices in the AI field. Last, we examine the effectiveness of altmetrics indices for different levels of AI journal papers and AI conference papers. Our results indicate that there is a rapid increase of AI publications and the public has paid more attention to AI research outputs since 2011. It is found that altmetrics indices are effective to discriminate highly cited publications and publications whose citation counts increase quickly. Among all Altmetric sub-indicators, Number of Mendeley readers is the most effective. Moreover, the results indicate that altmetrics indices are more effective in high levels of AI journal papers and AI conference papers. The main contribution of this paper is investigating the effectiveness of altmetrics indices from the perspective of different levels of publications. This study lays the foundation for further investigations about effectiveness of altmetrics indices from new perspectives, and it has important implication for the studies about the impact of social media on the scientific community.

Keywords: Altmetrics; Bibliometrics; Artificial intelligence; Highly cited publication; Increase of citation count; Citation analysis (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-019-03088-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:scient:v:119:y:2019:i:3:d:10.1007_s11192-019-03088-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-019-03088-x

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().