Abstract
Academic ranking prediction are indicators that have significant influences on the decision-making process of stakeholders of universities. In addition, we are in digital age with a pandemic situation, social media and technology have revolutionized the way scholars reach and disseminate academic outputs. Thus, the ranking consideration should be adjusted by augmented social perception data, e.g. Altmetrics. In this study, dataset of 1,752,494 research outputs from Altmetric.com and Scival.com which published between 2015–2020 are analyzed. This study assesses whether there are relationships between various scholarly output’s social perception data and citations. Moreover, various machine learning models are constructed to predict the citations. Results show weak to moderate positive correlation between social perception data and citation. We have found that the outperforming prediction model is Random Forest regression. The finding in our study suggested that social perception data should be considered to enhance academic ranking prediction in conjunction with related features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akella, A.P., Alhoori, H., Kondamudi, P.R., Freeman, C., Zhou, H.: Early indicators of scientific impact: Predicting citations with altmetrics. J. Informetrics 15(2), 101128 (2021). https://doi.org/10.1016/j.joi.2020.101128
Aksnes, D.W., Langfeldt, L., Wouters, P.: Citations, citation indicators, and research quality: an overview of basic concepts and theories. SAGE Open 9(1), 2158244019829575 (2019). https://doi.org/10.1177/2158244019829575
Bai, X., Zhang, F., Lee, I.: Predicting the citations of scholarly paper. J. Informetrics 13(1), 407–418 (2019). https://doi.org/10.1016/j.joi.2019.01.010
Banshal, S.K., Singh, V.K., Muhuri, P.K.: Can altmetric mentions predict later citations? A test of validity on data from ResearchGate and three social media platforms. Online Inf. Rev. 45(3), 517–536 (2020). https://doi.org/10.1108/OIR-11-2019-0364
Barbic, D., Tubman, M., Lam, H., Barbic, S.: An analysis of altmetrics in emergency medicine. Acad. Emerg. Med. 23(3), 251–265 (2016). https://doi.org/10.1111/acem.12898
Huang, W., Wang, P., Wu, Q.: A correlation comparison between Altmetric Attention Scores and citations for six PLOS journals. PLoS One 13(4), 1–15 (2018). https://doi.org/10.1371/journal.pone.0194962
Lehane, D.J., Black, C.S.: Can altmetrics predict future citation counts in critical care medicine publications? J. Intensive Care Soc. 22(1), 60–66 (2021). https://doi.org/10.1177/1751143720903240
Meschede, C.: The sustainable development goals in scientific literature: a bibliometric overview at the meta-level. Sustainability, 12(11), 4461 (2020). https://doi.org/10.3390/su12114461
Priem, J., Taraborelli, D., Groth, P., Neylon, C.: Altmetrics: a manifesto (2010). http://altmetrics.org/manifesto/. Accessed 18 Feb 2021
QS Quacquarelli Symonds Limited: Qs world university rankings by subject. https://www.topuniversities.com/subject-rankings/2021. Accessed 18 Feb 2021
Thelwall, M., Nevill, T.: Could scientists use Altmetric.com scores to predict longer term citation counts? J. Informetrics 12(1), 237–248 (2018). https://doi.org/10.1016/j.joi.2018.01.008
United Nations: take action for the sustainable development goals. https://www.un.org/sustainabledevelopment/sustainable-development-goals/. Accessed 18 Feb 2021
Wang, D., Song, C., Barabási, A.L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013). https://doi.org/10.1126/science.1237825, https://science.sciencemag.org/content/342/6154/127
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chantaranimi, K., Sugunsil, P., Natwichai, J. (2022). An Approach to Enhance Academic Ranking Prediction with Augmented Social Perception Data. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-84910-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84909-2
Online ISBN: 978-3-030-84910-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)