|
|
|
| Review of Scientific Impact Prediction |
| Huo Chaoguang1, Dong Ke2, Wei Ruibin3 |
1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.School of Information Management, Wuhan University, Wuhan 430072
3.School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030 |
|
|
|
|
Abstract Driven by data, research on scientific impact prediction is flourishing. As an important part of data-driven prediction in science of science, scientific impact prediction focuses on the future impact prediction of papers, scholars, journals, and institutions on academic entities and aims to provide suggestions for scientific research and scientific management. This paper illustrated the research development of paper impact prediction, scholar impact prediction, journal impact prediction, and institution impact prediction and summarized the features of each kind of prediction. The study proposed two method frameworks and provided an overview on the research trends of indicators, methods, and features in scientific impact prediction. With the development of data elements, data opening, and data sharing, scientific impact prediction will be further improved, leveraging new comprehensive indicators and featuring mining algorithms and time series prediction methods.
|
|
Received: 08 June 2020
|
|
|
|
1 郭凤娇, 赵蓉英, 孙劭敏. 基于科学交流过程的学术论文影响力评价研究——以中国社会科学国际学术论文为例[J]. 情报学报, 2020, 39(4): 357-366.
2 赵蓉英, 郭凤娇, 谭洁. 基于Altmetrics的学术论文影响力评价研究——以汉语言文学学科为例[J]. 中国图书馆学报, 2016, 42(1): 96-108.
3 Zhang F L, Bai X M, Lee I. Author impact: evaluations, predictions, and challenges[J]. IEEE Access, 2019, 7: 38657-38669.
4 Hou J, Pan H X, Guo T, et al. Prediction methods and applications in the science of science: a survey[J]. Computer Science Review, 2019, 34: 100197.
5 Wu Z M, Lin W W, Liu P, et al. Predicting long-term scientific impact based on multi-field feature extraction[J]. IEEE Access, 2019, 7: 51759-51770.
6 Bai X M, Zhang F L, Hou J, et al. Implicit multi-feature learning for dynamic time series prediction of the impact of institutions[J]. IEEE Access, 2017, 5: 16372-16382.
7 Liu X, Yan J, Xiao S, et al. On predictive patent valuation: forecasting patent citations and their types[C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 1438-1444.
8 Fortunato S, Bergstrom C T, B?rner K, et al. Science of science[J]. Science, 2018, 359(6379): eaao0185.
9 Clauset A, Larremore D B, Sinatra R. Data-driven predictions in the science of science[J]. Science, 2017, 355(6324): 477-480.
10 Montáns F J, Chinesta F, Gómez-Bombarelli R, et al. Data-driven modeling and learning in science and engineering[J]. Comptes Rendus Mécanique, 2019, 347(11): 845-855.
11 王海燕, 潘云涛, 马峥, 等. 基于科学研究问题成熟度的未来高影响力科技论文预测研究[J]. 情报学报, 2016, 35(1): 36-47.
12 Ke Q, Ferrara E, Radicchi F, et al. Defining and identifying Sleeping Beauties in science[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(24): 7426-7431.
13 Bai X M, Zhang F L, Lee I. Predicting the citations of scholarly paper[J]. Journal of Informetrics, 2019, 13(1): 407-418.
14 Xu J G, Li M J, Jiang J, et al. Early prediction of scientific impact based on multi-bibliographic features and convolutional neural network[J]. IEEE Access, 2019, 7: 92248-92258.
15 Li M, Xu J G, Ge B F, et al. A deep learning methodology for citation count prediction with large-scale biblio-features[C]// Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2019: 1172-1176.
16 Yuan S, Tang J, Zhang Y, et al. Modeling and predicting citation count via recurrent neural network with long short-term memory[OL]. (2018-11-06). https://arxiv.org/pdf/1811.02129.pdf.
17 Abrishami A, Aliakbary S. Predicting citation counts based on deep neural network learning techniques[J]. Journal of Informetrics, 2019, 13(2): 485-499.
18 Priem J, Groth P, Taraborelli D. The altmetrics collection[J]. PLoS ONE, 2012, 7(11): e48753.
19 Eysenbach G. Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact[J]. Journal of Medical Internet Research, 2011, 13(4): e123.
20 Hassan S U, Aljohani N R, Idrees N, et al. Predicting literature’s early impact with sentiment analysis in Twitter[J]. Knowledge-Based Systems, 2020, 192: 105383.
21 Thelwall M, Haustein S, Larivière V, et al. Do altmetrics work? Twitter and ten other social web services[J]. PLoS ONE, 2013, 8(5): e64841.
22 Shema H, Bar-Ilan J, Thelwall M. Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics[J]. Journal of the Association for Information Science and Technology, 2014, 65(5): 1018-1027.
23 李纲, 管为栋, 马亚雪, 等. 学术论文的社交媒体可见性预测研究[J]. 数据分析与知识发现, 2020, 4(8): 63-74.
24 Singh M, Patidar V, Kumar S, et al. The role of citation context in predicting long-term citation profiles: an experimental study based on a massive bibliographic text dataset[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York: ACM Press, 2015: 1271-1280.
25 Yu T, Yu G, Li P Y, et al. Citation impact prediction for scientific papers using stepwise regression analysis[J]. Scientometrics, 2014, 101(2): 1233-1252.
26 Haslam N, Ban L, Kaufmann L, et al. What makes an article influential? Predicting impact in social and personality psychology[J]. Scientometrics, 2008, 76(1): 169-185.
27 Bornmann L, Schier H, Marx W, et al. What factors determine citation counts of publications in chemistry besides their quality?[J]. Journal of Informetrics, 2012, 6(1): 11-18.
28 Robson B J, Mousquès A. Can we predict citation counts of environmental modelling papers? Fourteen bibliographic and categorical variables predict less than 30% of the variability in citation counts[J]. Environmental Modelling & Software, 2016, 75: 94-104.
29 Sohrabi B, Iraj H. The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts[J]. Scientometrics, 2017, 110(1): 243-251.
30 夏琬钧, 任鹏, 陈晓红. 学者影响力预测研究综述[J]. 情报理论与实践, 2020, 43(7): 165-170.
31 Pan R K, Fortunato S. Author Impact Factor: tracking the dynamics of individual scientific impact[J]. Scientific Reports, 2014, 4: 4880.
32 Hirsch J E. An index to quantify an individual's scientific research output[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(46): 16569-16572.
33 Sinatra R, Wang D S, Deville P, et al. Quantifying the evolution of individual scientific impact[J]. Science, 2016, 354(6312): aaf5239.
34 Bornmann L, Williams R. Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data[J]. Journal of Informetrics, 2017, 11(3): 788-799.
35 Hirsch J E. Does the h index have predictive power?[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(49): 19193-19198.
36 Acuna D E, Allesina S, Kording K P. Predicting scientific success[J]. Nature, 2012, 489(7415): 201-202.
37 Ayaz S, Masood N, Islam M A. Predicting scientific impact based on h-index[J]. Scientometrics, 2018, 114(3): 993-1010.
38 Mistele T, Price T, Hossenfelder S. Predicting authors’ citation counts and h-indices with a neural network[J]. Scientometrics, 2019, 120(1): 87-104.
39 Nezhadbiglari M, Gon?alves M A, Almeida J M. Early prediction of scholar popularity[C]// Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. New York: ACM Press, 2016: 181-190.
40 Panagopoulos G, Tsatsaronis G, Varlamis I. Detecting rising stars in dynamic collaborative networks[J]. Journal of Informetrics, 2017, 11(1): 198-222.
41 Zhang J, Hu Y, Ning Z L, et al. AIRank: author impact ranking through positions in collaboration networks[J]. Complexity, 2018, 2018: Article ID 4697485.
42 Zhang J, Ning Z L, Bai X M, et al. Who are the rising stars in academia?[C]// Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. New York: ACM Press, 2016: 211-212.
43 Zhang J, Xu B, Liu J Y, et al. PePSI: personalized prediction of scholars’ impact in heterogeneous temporal academic networks[J]. IEEE Access, 2018, 6: 55661-55672.
44 Daud A, Ahmad M F, Malik M S I, et al. Using machine learning techniques for rising star prediction in co-author network[J]. Scientometrics, 2015, 102(2): 1687-1711.
45 Sandulescu V, Chiru M. Predicting the future relevance of research institutions-the winning solution of the KDD Cup 2016[OL]. (2016-09-09). https://arxiv.org/pdf/1609.02728.pdf.
46 Xie J. Predicting institution-level paper acceptance at conferences: a time-series regression approach[C]// Proceedings of the KDD Cup Workshop. 2016: 1-6.
47 Wu X F, Fu Q, Rousseau R. On indexing in the Web of Science and predicting journal impact factor[J]. Journal of Zhejiang University Science B, 2008, 9(7): 582-590.
48 李秀霞, 邵作运. 基于论文作者特征的期刊影响力预测[J]. 中国科技期刊研究, 2017, 28(4): 344-349.
49 张耀辉, 周森鑫, 李超. 多态有奖马尔可夫学术期刊动态评价模型研究[J]. 情报理论与实践, 2016, 39(5): 46-52, 39.
50 丁筠. 学术期刊影响力指数(CI)预测模型的构建[J]. 情报科学, 2017, 35(2): 27-32, 37.
51 Ebadi A, Tremblay S, Goutte C, et al. Application of machine learning techniques to assess the trends and alignment of the funded research output[J]. Journal of Informetrics, 2020, 14(2): 101018.
52 朱卫东, 刘芳, 王东鹏, 等. 科学基金项目立项评估: 综合评价信息可靠性的多指标证据推理规则研究[J]. 中国管理科学, 2016, 24(10): 141-148.
53 马瑞敏, 尉心渊. 技术领域细分视角下核心专利预测研究[J]. 情报学报, 2017, 36(12): 1279-1289.
54 B?rner K, Rouse W B, Trunfio P, et al. Forecasting innovations in science, technology, and education[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(50): 12573-12581.
55 Maggio L A, Meyer H S, Artino A R. Beyond citation rates: a real-time impact analysis of health professions education research using altmetrics[J]. Academic Medicine, 2017, 92(10): 1449-1455.
56 Aksnes D W, Langfeldt L, Wouters P. Citations, citation indicators, and research quality: an overview of basic concepts and theories[J]. SAGE Open, 2019, 9(1): 2158244019829575.
57 Ortega J L. The coverage of blogs and news in the three major altmetric data providers[C]// Proceedings of the 17th International Conference on Scientometrics and Informetrics, 2019: 75-86.
58 余厚强, 别克扎提·木拉提. 从ISSI 2019会议解读替代计量学研究新进展[J]. 情报理论与实践, 2020, 43(7): 157-164.
59 Hirsch J E. Superconductivity, what the H? The emperor has no clothes[J]. APS Forum on Physics and Society Newsletter, 2020, 49(1): 4-9.
60 Conroy G. What’s wrong with the h-index, according to its inventor[EB/OL]. (2020-03-24) [2020-05-21]. https://www.natureindex.com/news-blog/whats-wrong-with-the-h-index-according-to-its-inventor.
61 Sohrabi B, Iraj H. The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts[J]. Scientometrics, 2017, 110(1): 243-251.
62 Chen J P, Zhang C X. Predicting citation counts of papers[C]// Proceedings of the 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing. IEEE, 2015: 434-440.
63 Abramo G, D’Angelo C A, Felici G. Predicting publication long-term impact through a combination of early citations and journal impact factor[J]. Journal of Informetrics, 2019, 13(1): 32-49.
64 Wu Z H, Pan S R, Chen F W, et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4-24.
65 Cheng Z Q, Yang Y, Wang W, et al. Time2Graph: revisiting time series modeling with dynamic shapelets[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 3617-3624.
66 Zhang J W, Yu P S. Broad learning: an emerging area in social network analysis[J]. ACM SIGKDD Explorations Newsletter, 2018, 20(1): 24-50.
67 Long F Y, Ning N W, Song C G, et al. Strengthening social networks analysis by networks fusion[C]// Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM Press, 2019: 460-463.
68 黄炜, 童青云, 李岳峰. 广度学习研究进展:基于情报学的视角[J]. 情报理论与实践, 2020, 43(4): 177-185.
69 阎光才. 学术影响力评价的是非争议[J]. 教育研究, 2019, 40(6): 16-26.
70 周涛. 预测的局限性[J]. 大数据, 2017, 3(4): 104-108. |
|
|
|
