Title: University ranking approach with bibliometrics and augmented social perception data

Authors: Kittayaporn Chantaranimi; Rattasit Sukhahuta; Juggapong Natwichai

Addresses: Data Science Consortium, Faculty of Engineering, Chiang Mai University, Thailand ' Department of Computer Science, Faculty of Science, Chiang Mai University, Thailand ' Department of Computer Engineering, and Data Science Consortium, Faculty of Engineering, Chiang Mai University, Thailand

Abstract: Typically, universities aim to achieve a high position in ranking systems for their reputation. However, self-evaluating rankings could be costly because the indicators are not only from bibliometrics, but also the results of over a thousand surveys. In this paper, we propose a novel approach to estimate university rankings based on traditional data, i.e., bibliometrics, and non-traditional data, i.e., Altmetric Attention Score, and Sustainable Development Goals indicators. Our approach estimates subject-areas rankings in Arts & Humanities, Engineering & Technology, Life Sciences & Medicine, Natural Sciences, and Social Sciences & Management. Then, by using Spearman rank-order correlation and overlapping rate, our results are evaluated by comparing with the QS subject ranking. From the result, our approach, particularly the top-10 ranking, performed estimating effectively and then could assist stakeholders in estimating the university's position when the survey is not available.

Keywords: university ranking; rank similarity; bibliometrics; augmented social perception data; sustainable development goals; altmetrics.

DOI: 10.1504/IJGUC.2026.152666

International Journal of Grid and Utility Computing, 2026 Vol.17 No.2, pp.99 - 115

Received: 23 Jun 2022
Received in revised form: 04 Jul 2022
Accepted: 05 Jul 2022
Published online: 07 Apr 2026 *

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