Abstract
A significant aspect in scientific research is evaluating the impact of scientific contribution of an author. Authors select a particular Field of Study (FoS) to work in, based on their interest and/or by considering the currently hot topics. One aspect that may support in the selection of their research area is the future impact of their work. There are multiple studies in literature that focus on FoS trend detection and analysis etc. However, the previous work contains a gap, that is, effect of following an FoS on citation trend of scientific authors is not explored. The outcome of such a study may help a researcher in deciding his future research direction. In this research, we have proposed an approach that detects the FoS of an individual author in his/her career years by characterizing the focus of his work. Citation trend of an author based on yearly citation count is computed. The trend of an FoS is computed by combining citation trend of authors belonging to same FoS. This trend is used as an input to predict next years’ citation counts, by using Multiple Linear Regression and Artificial Neural Network. The scientific articles published from 1950 to 2017 in the domain of Computer Science are used from Microsoft Academic Graph dataset. The experimental results show that there is higher similarity between citation trend of authors that belong to the same FoS as compared to different FoS and also resembles more to the overall citation trend of that particular FoS. Hence concluded that FoS trend following has a certain impact on the citation count of authors.
Similar content being viewed by others
References
Allan, J., Carbonell, J. G., Doddington, G., Yamron, J., & Yang, Y. (1998). Topic detection and tracking pilot study final report. In Proceedings of the DARPA broadcast news transcription and understanding workshop.
Amjad, T., Ding, Y., Xu, J., Zhang, C., Daud, A., Tang, J., & Song, M. (2017). Standing on the shoulders of giants. Journal of Informetrics, 11(1), 307–323.
Blei, D., & Lafferty, J. (2006). Correlated topic models. Advances in Neural Information Processing Systems, 18, 147.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Bolelli, L., Ertekin, Ş, & Giles, C. L. (2009). Topic and trend detection in text collections using latent dirichlet allocation. European conference on information retrieval (pp. 776–780). Berlin: Springer.
Bourdieu, P. (1975). The specificity of the scientific field and the social conditions of the progress of reason. Social Science Information, 14(6), 19–47.
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.
Clauset, A., Larremore, D. B., & Sinatra, R. (2017). Data-driven predictions in the science of science. Science, 355(6324), 477–480.
De Domenico, M., Omodei, E., & Arenas, A. (2016). Quantifying the diaspora of knowledge in the last century. Applied Network Science, 1(1), 1–13.
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.
Deville, P., Wang, D., Sinatra, R., Song, C., Blondel, V. D., & Barabási, A. L. (2014). Career on the move: Geography, stratification and scientific impact. Scientific Reports, 4(1), 1–7.
Eberly, L. E. (2007). Multiple linear regression. Topics in Biostatistics, 404, 165–187.
Effendy, S., & Yap, R. H. (2017, April). Analysing trends in computer science research: A preliminary study using the microsoft academic graph. In Proceedings of the 26th international conference on world wide web companion (pp. 1245–1250).
Eichmann, D., Ruiz, M., Srinivasan, P., Street, N., Culy, C., & Menczer, F. (1999, February). A cluster-based approach to tracking, detection and segmentation of broadcast news. In Proceedings of the DARPA Broadcast News Workshop (pp. 69–76).
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., & Barabási, A. L. (2018). Science of science. Science, 359(6379), eaao0185.
Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908.
Ho, J. C., Saw, E. C., Lu, L. Y., & Liu, J. S. (2014). Technological barriers and research trends in fuel cell technologies: A citation network analysis. Technological Forecasting and Social Change, 82, 66–79.
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).
Hoonlor, A., Szymanski, B. K., & Zaki, M. J. (2013). Trends in computer science research. Communications of the ACM, 56(10), 74–83.
Jia, T., Wang, D., & Szymanski, B. K. (2017). Quantifying patterns of research-interest evolution. Nature Human Behaviour, 1(4), 1–7.
Jones, B. F., & Weinberg, B. A. (2011). Age dynamics in scientific creativity. Proceedings of the National Academy of Sciences, 108(47), 18910–18914.
Kleinberg, J. (2002). Bursty and hierarchical structure in streams, data mining and knowledge discovery. In: Elected papers from the 8th ACM SIGKDD International Conference on Knowledge I Discovery and Data Mining? Part (Vol. 7, No. 4, pp. 372–397).
Kuhn, T., Perc, M., & Helbing, D. (2014). Inheritance patterns in citation networks reveal scientific memes. Physical Review X, 4(4), 041036.
Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., & Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714), 396–399.
Merton, R. K. (1968). The Matthew effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56–63.
Petersen, A. M. (2015). Quantifying the impact of weak, strong, and super ties in scientific careers. Proceedings of the National Academy of Sciences, 112(34), E4671–E4680.
Petersen, A. M. (2018). Multiscale impact of researcher mobility. Journal of the Royal Society Interface, 15(146), 20180580.
Petersen, A. M., Fortunato, S., Pan, R. K., Kaski, K., Penner, O., Rungi, A., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2014). Reputation and impact in academic careers. Proceedings of the National Academy of Sciences, 111(43), 15316–15321.
Petersen, A. M., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2012). Persistence and uncertainty in the academic career. Proceedings of the National Academy of Sciences, 109(14), 5213–5218.
Qi, M., Zeng, A., Li, M., Fan, Y., & Di, Z. (2017). Standing on the shoulders of giants: The effect of outstanding scientists on young collaborators’ careers. Scientometrics, 111(3), 1839–1850.
Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2012). The author-topic model for authors and documents. arXiv Preprint. https://doi.org/10.48550/arXiv.1207.4169
Rzhetsky, A., Foster, J. G., Foster, I. T., & Evans, J. A. (2015). Choosing experiments to accelerate collective discovery. Proceedings of the National Academy of Sciences, 112(47), 14569–14574.
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513–523.
Schultz, J. M., & Liberman, M. (1999, February). Topic detection and tracking using idf-weighted cosine coefficient. In: Proceedings of the DARPA broadcast news workshop (Vol. 1892192). San Francisco: Morgan Kaufmann.
Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.
Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B. J., & Wang, K. (2015, May). An overview of microsoft academic service (mas) and applications. In: Proceedings of the 24th international conference on world wide web (pp. 243–246).
Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004, August). Probabilistic author-topic models for information discovery. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 306–315).
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008, August). Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 990–998).
Wang, C., Blei, D., & Heckerman, D. (2012). Continuous time dynamic topic models. arXiv Preprint. https://doi.org/10.48550/arXiv.1206.3298
Zeng, A., Shen, Z., Zhou, J., Fan, Y., Di, Z., Wang, Y., Stanley, H. E., & Havlin, S. (2019). Increasing trend of scientists to switch between topics. Nature Communications, 10(1), 1–11.
Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., & Stanley, H. E. (2017). The science of science: From the perspective of complex systems. Physics Reports, 714, 1–73.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zafar, L., Masood, N. & Ayaz, S. Impact of field of study (FoS) on authors’ citation trend. Scientometrics 128, 2557–2576 (2023). https://doi.org/10.1007/s11192-023-04660-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-023-04660-2