Prediction of high-impact research: a historical review and research opportunities

Abramo, G., D’Angelo, C. A., & Felici, G. (2019). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32–49.

Article  Google Scholar 

Aksnes, D. W. (2003). Characteristics of highly cited papers. Research Evaluation, 12(3), 159–170.

Article  Google Scholar 

Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.

Article  Google Scholar 

Bai, X., Zhang, F., & Lee, I. (2019). Predicting the citations of scholarly paper. Journal of Informetrics, 13(1), 407–418.

Article  Google Scholar 

Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

Article  MathSciNet  Google Scholar 

Behrouzi, S., Sarmoor, Z. S., Hajsadeghi, K., & Kavousi, K. (2020). Predicting scientific research trends based on link prediction in keyword networks. Journal of Informetrics, 14(4), Article 101079.

Article  Google Scholar 

Bertsimas, D., Brynjolfsson, E., Reichman, S., & Silberholz, J. (2013). Network analysis for predicting academic impact. In ICIS.

Billah, S. M., & Gauch, S. (2015, November). Social network analysis for predicting emerging researchers. In 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K) (Vol. 1, pp. 27–35). IEEE.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

Google Scholar 

Blümel, C., & Schniedermann, A. (2020). Studying review articles in scientometrics and beyond: A research agenda. Scientometrics, 124, 711–728.

Article  Google Scholar 

Bollen, J., Rodriquez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69, 669–687.

Article  Google Scholar 

Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.

Article  Google Scholar 

Bornmann, L., Leydesdorff, L., & Wang, J. (2014). How to improve the prediction based on citation impact percentiles for years shortly after the publication date? Journal of Informetrics, 8(1), 175–180.

Article  Google Scholar 

Bornmann, L., & Marx, W. (2015). Methods for the generation of normalized citation impact scores in bibliometrics: Which method best reflects the judgements of experts? Journal of Informetrics, 9(2), 408–418.

Article  Google Scholar 

Braun, T., Bergstrom, C. T., Frey, B. S., Osterloh, M., West, J. D., Pendlebury, D., & Rohn, J. (2010). How to improve the use of metrics. Nature, 465(17), 870–872.

Google Scholar 

Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.

Article  Google Scholar 

Chan, H. F., Mixon, F. G., & Torgler, B. (2018). Relation of early career performance and recognition to the probability of winning the Nobel Prize in economics. Scientometrics, 114, 1069–1086.

Article  Google Scholar 

Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386–1409.

Article  Google Scholar 

Chen, H., Zhang, G., Zhu, D., & Lu, J. (2017). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technological Forecasting and Social Change, 119, 39–52.

Article  Google Scholar 

Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific gems with Google’s PageRank algorithm. Journal of Informetrics, 1(1), 8–15.

Article  Google Scholar 

Chen, S., Arsenault, C., & Larivière, V. (2015). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034–1046.

Article  Google Scholar 

Cheng, Q., Wang, J., Lu, W., Huang, Y., & Bu, Y. (2020). Keyword-citation-keyword network: A new perspective of discipline knowledge structure analysis. Scientometrics, 124, 1923–1943.

Article  Google Scholar 

Choi, J., Yi, S., & Lee, K. C. (2011). Analysis of keyword networks in MIS research and implications for predicting knowledge evolution. Information & Management, 48(8), 371–381.

Article  Google Scholar 

Chung, P., & Sohn, S. Y. (2020). Early detection of valuable patents using a deep learning model: Case of semiconductor industry. Technological Forecasting and Social Change, 158, Article 120146.

Article  Google Scholar 

Cole, S., Cole, J. R., & Simon, G. A. (1981). Chance and consensus in peer review. Science, 214(4523), 881–886.

Article  Google Scholar 

Cronin, B. (1984). The citation process: The role and significance of citations in scientific communication. Taylor Graham.

Google Scholar 

de Winter, J. (2024). Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among scientific abstracts. Scientometrics. https://doi.org/10.1007/s11192-024-04939-y

Article  Google Scholar 

Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861–873.

Article  Google Scholar 

Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014). Content-based citation analysis: The next generation of citation analysis. Journal of the Association for Information Science and Technology, 65(9), 1820–1833.

Article  Google Scholar 

Dvořáčková, L., Joachimiak, M. P., Černý, M., Kubecová, A., Sklenák, V., & Kliegr, T. (2025). Explaining word embeddings with perfect fidelity: A case study in predicting research impact. Machine Learning, 114(12), 1–28.

Article  MathSciNet  Google Scholar 

Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.

Article  MathSciNet  Google Scholar 

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308.

Article  Google Scholar 

Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95, 225–242.

Article  Google Scholar 

Ernst, E., & Resch, K. L. (1993). Fibrinogen as a cardiovascular risk factor: A meta-analysis and review of the literature. Annals of Internal Medicine, 118(12), 956–963.

Article  Google Scholar 

Eubanks, D. L., Palanski, M. E., Swart, J., Hammond, M. M., & Oguntebi, J. (2014). Time to create: Pathways to earlier and later creative discoveries in Nobel prize winners. Creativity and leadership in science, technology, and innovation (pp. 184–208). Routledge.

Google Scholar 

Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.

Article  Google Scholar 

Fu, L., & Aliferis, C. (2010). Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientometrics, 85(1), 257–270.

Article  Google Scholar 

Fu, L. D., & Aliferis, C. (2008). Models for predicting and explaining citation count of biomedical articles. In AMIA Annual Symposium Proceedings 2008 (p.222).

Google Scholar 

Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.

Article  Google Scholar 

Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1 (4), 359–375.

Article  Google Scholar 

Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 3(3), 173–179.

Article  Google Scholar 

González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379–391.

Article  Google Scholar 

Google Scholar Blog. (2011). Google Scholar citations open to all. Published 16 November 2011. Retrieved June 20, 2016, from https://scholar.googleblog.com/2011/11/google-scholar-citations-open-to-all.html

Gross, P. L., & Gross, E. M. (1927). College libraries and chemical education. Science, 66(1713), 385–389.

Article  Google Scholar 

Gu, X., & Krenn, M. (2024). Forecasting high-impact research topics via machine learning on evolving knowledge graphs. arXiv preprint arXiv:2402.08640.

Haghighat, M., & Hayatdavoudi, J. (2021). How hot are hot papers? The issue of prolificacy and self-citation stacking. Scientometrics, 126(1), 565–578.

Article  Google Scholar 

Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., Whelan, J., & Wilson, S. (2008). What makes an article influential? Predicting impact in social and personality psychology. Scientometrics, 76(1), 169–185.

Article  Google Scholar 

Hirsch, J. E. (2007). Does the h index have predictive power? Proceedings of the National Academy of Sciences, 104(49), 19193–19198.

Article  Google Scholar 

Hofmann, T. (1999, August). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 50–57).

Hou, J., Pan, H., Guo, T., Lee, I., Kong, X., & Xia, F. (2019). Prediction methods and applications in the science of science: A survey. Computer Science Review, 34, Article 100197.

Article 

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