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|Title:||A comparative investigation on citation counts and altmetrics between papers authored by top universities and companies in the research field of artificial intelligence||Authors:||Luo, Feiheng
Sesagiri Raamkumar, Aravind
|Issue Date:||2018||Source:||Luo, F., Zheng, H., Erdt, M., Sesagiri Raamkumar, A., & Theng, Y.-L. (2018). A comparative investigation on citation counts and altmetrics between papers authored by top universities and companies in the research field of artificial intelligence. AROSIM 2018: Altmetrics for Research Outputs Measurement and Scholarly Information Management, 105-114.||Abstract:||Artificial Intelligence is currently a popular research field. With the development of deep learning techniques, researchers in this area have achieved impressive results in a variety of tasks. In this initial study, we explored scientific papers in Artificial Intelligence, making comparisons between papers authored by the top universities and companies from the dual perspectives of bibliometrics and altmetrics. We selected publication venues according to the venue rankings provided by Google Scholar and Scopus, and retrieved related papers along with their citation counts from Scopus. Altmetrics such as Altmetric Attention Scores and Mendeley reader counts were collected from Altmetric.com and PlumX. Top universities and companies were identified, and the retrieved papers were classified into three groups accordingly: university-authored papers, company-authored papers, and co-authored papers. Comparative results showed that university-authored papers received slightly higher citation counts than company-authored papers, while company-authored papers gained considerably more attention online. In addition, when we focused on the most impactful papers, i.e., the papers with the highest numbers of citation counts, and the papers with the largest amount of online attention, companies seemed to make a larger contribution by publishing more impactful papers than universities.||URI:||https://hdl.handle.net/10356/79727
|DOI:||10.1007/978-981-13-1053-9_9||Rights:||© 2018 Springer Nature Singapore Pte Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by AROSIM 2018: Altmetrics for Research Outputs Measurement and Scholarly Information Management, Springer Nature Singapore Pte Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/978-981-13-1053-9_9].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||WKWSCI Conference Papers|
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