Predicting citation counts based on deep neural network learning techniques |
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| Institution: | 1. National Taiwan University of Science and Technology, Graduate Institute of Patent, No. 43, Sec. 4, Keelung Rd., Taipei, Taiwan, ROC;2. National Taiwan University, Department of Mechanical Engineering, No. 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan, ROC;3. National Taiwan University, Department of Library and Information Science, No. 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan, ROC;4. National Taiwan University, Center for Research in Econometric Theory and Applications (CRETA), No. 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan, ROC;1. Research Group of Operations Research and Decision Systems, Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary;2. Department of Operations Research and Actuarial Sciences Corvinus University of Budapest (BCE), Budapest, Hungary;1. Department of Economics, University of Maryland, College Park, MD 20742, United States;2. NBER, Cambridge, MA, United States;3. Department of Computer Science, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, C1428EGA, Argentina;1. School of Business, University of New South Wales, Canberra, Northcott Dr, Campbell ACT 2612, Australia;2. School of Engineering and Information Technology, University of New South Wales, Canberra, Northcott Dr, Campbell ACT 2612, Australia;1. Laboratory for Studies in Research Evaluation, Institute for System Analysis and Computer Science (IASI-CNR), National Research Council of Italy, Istituto di Analisi dei Sistemi e Informatica, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Roma, Italy;2. University of Rome “Tor Vergata” - Italy, Laboratory for Studies in Research Evaluation (IASI-CNR), Dipartimento di Ingegneria dell’Impresa, Università degli Studi di Roma “Tor Vergata”, Via del Politecnico 1, 00133 Roma, Italy;3. Research Value s.r.l., Via Michelangelo Tilli 39, 00156 Roma, Italy;1. Department of Physics, University of Fribourg, Fribourg 1700, Switzerland;2. Fintech Research Institute, Shanghai University of Finance and Economics, Shanghai 200433, PR China;3. School of Systems Science, Beijing Normal University, Beijing 100875, PR China;4. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, PR China;5. Department of Radiation Oncology, Inselspital, University Hospital of Bern and University of Bern, Bern 3010, Switzerland |
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| Abstract: | With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations. |
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| Keywords: | Informetrics Citation count prediction Neural networks Deep learning Scientific impact Time series prediction |
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