Can we predict ESI highly cited publications?
The highly cited papers defined by Clarivate Analytics’ Essential Science Indicators (ESI) have been widely used to measure the scientific performance of scientists, research institutions, universities and countries. However, researchers have seldom studied which factors can affect a paper to be an ESI highly cited paper. The prediction of ESI highly cited papers is much less studied, too. According to the existing researches about factors influencing paper’s citations, four classical papers’ factors are chosen in this study, which are scientific impact of the first author, scientific impact of the potential leader, scientific impact of the team and the relevance of authors’ existing papers. Similar to the definition of ESI highly cited papers, we develop a new measure of papers’ scientific impact. Firstly, we get statistics properties of four factors with APS data and Nobel data in order to study four factors’ performance of ESI highly cited papers. Then, Spearman correlation and Logistic regression are applied to explore the relationship between four factors and papers’ scientific impact. At last, we try to predict highly cited papers by NN algorithms incorporating four factors. The results show that the potential leader factor plays a more important role in the short term than in the long term, while the team factor is on the contrary, more important in the long term. Interestingly, the first author factor doesn’t have an obvious effect on papers’ scientific impact among top 1%. The prediction results are better than random.
KeywordsESI Citation network Scientific impact Prediction
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61603046 and 61374175) and the Natural Science Foundation of Beijing (Grant No. L160008).
- Danell, R. (2011). Can the quality of scientific work be predicted using information on the author’s track record? Journal of the Association for Information Science and Technology, 62(1), 50–60.Google Scholar
- Hurley, L. A., Ogier, A. L., & Torvik, V. I. (2013, November). Deconstructing the collaborative impact: Article and author characteristics that influence citation count. In Proceedings of the 76th ASIS&T annual meeting: Beyond the cloud: Rethinking information boundaries (p. 61). American Society for Information Science.Google Scholar
- Xiao, S., Yan, J., Li, C., Jin, B., Wang, X., Yang, X., et al. (2016, July). On modeling and predicting individual paper citation count over time. In IJCAI (pp. 2676–2682).Google Scholar
- Yan, R., Tang, J., Liu, X., Shan, D., & Li, X. (2011, October). Citation count prediction: Learning to estimate future citations for literature. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1247–1252). ACM.Google Scholar