Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity |
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| Institution: | 1. Department of Information Management, National Central University, Taoyuan City, Taiwan 320, R.O.C.;2. Center for Innovative Research on Aging Society, National Chung Cheng University, Chiayi, Taiwan 621, R.O.C.;3. MOST AI Biomedical Research Center at National Cheng Kung University, Taiwan 701, R.O.C.;4. Department of Information Management, National Chung Cheng University, Chiayi, Taiwan 621, R.O.C.;5. Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan 613, R.O.C.;6. Department of Agricultural Economics, National Taiwan University Taipei, Taiwan 106, R.O.C.;1. International Joint Informatics Laboratory (IJIL), Nanjing University, Nanjing, 210023, China;2. International Joint Informatics Laboratory (IJIL), University of Illinois, Champaign, United States;3. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, School of Information Management, Nanjing University, Nanjing, 210023, China;1. Library, Nanjing Medical University, Nanjing, 210029, China;2. State Key Laboratory of Analytical Chemistry for Life Science, School of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China;1. National Research University Higher School of Economics, Myasnitskaya, 20, 101000 Moscow, Russia;2. Institute of Mathematics, Physics and Mechanics, Jadranska 19, 1000 Ljubljana, Slovenia;3. University of Primorska, Andrej Maru?i? Institute, 6000 Koper, Slovenia;1. Center for Modern Korean Studies, Yonsei University, Wonju, Republic of Korea;2. Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea;3. College of Computing and Informatics, Drexel University, Philadelphia, USA;1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, PR China;2. URPP Social Networks, University of Zurich, 8050 Zurich, Switzerland;3. Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, 311121 Hangzhou, PR China;4. Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland;5. Department of Physics, University of Fribourg, 1700 Fribourg, Switzerland;1. A P J Abdul Kalam Technological University, Thiruvananthapuram, Kerala, 695016, India;2. CSIR Central Mechanical Engineering Research Institute, Durgapur, Bengal, 713209, India;3. Amsterdam School of Communication Research (ASCoR), University of Amsterdam, PO Box 15793, 1001 NG Amsterdam, the Netherlands |
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| Abstract: | The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline. |
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| Keywords: | highly-cited papers keyword popularity supervised learning binary classification topic model |
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