Predicting the citation counts of individual papers via a BP neural network |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing, 210023, China;2. School of Management and Engineering, Nanjing University, Nanjing, 210023, China;1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;2. Department of Mathematics, University of California, Los Angeles, USA;1. Department of Computer Science, University of South Alabama, 307 N University Blvd, Mobile, AL 36688, United States;2. Department of Knowledge Service Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea;1. Department of Physics, University of Fribourg, Fribourg 1700, Switzerland;2. School of Systems Science, Beijing Normal University, Beijing, 100875, PR China;1. School of Computer Science, Jiangsu University, China;2. School of Computing, Ulster University, UK;1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;2. Department of Mathematics, University of California, Los Angeles, USA;1. School of Information, Shanxi University of Finance & Economics, 030006, Taiyuan, China;2. CNKI, Inc., 100192, Beijing, China |
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| Abstract: | Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction. |
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| Keywords: | Citation prediction Neural network XGBoost Linear regression |
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