Thousands of research papers are being published every day, and among all these research works, one of the fastest-growing fields is computer science (CS). Thus, learning which research areas are trending in this particular field of study is advantageous to a significant number of scholars, research institutions, and funding organizations. Many scientometric studies have been done focusing on analyzing the current CS trends and predicting future ones from different perspectives as a consequence. Despite the large datasets from this vast number of CS publications and the power of deep learning methods in such big data problems, deep neural networks have not yet been used to their full potential in this area. Therefore, the objective of this paper is to predict the upcoming years’ CS trends using long short-term memory neural networks. Accordingly, CS papers from 1940 and their corresponding fields of study from the microsoft academic graph dataset have been exploited for solving this research trend prediction problem. The prediction accuracy of the proposed method is then evaluated using RMSE and coefficient of determination (R2) metrics. The evaluations show that the proposed method outperforms the baseline approaches in terms of the prediction accuracy in all considered time periods. Subsequently, adopting the proposed method’s predictions, we investigate future trending areas in computer science research from various viewpoints.
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Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.
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), 101079.
Brockwell, P. J., & Davis, R. A. (2013). Stationary ara processes, time series. Theory and Methods, 3, 77–110.
Chen, C., Wang, Z., Li, W., & Sun, X. (2018) Modeling scientific influence for research trending topic prediction. In AAAI (pp. 2111–2118).
Cheng, Q., Xin, L., Liu, Z., & Huang, J. (2015). Mining research trends with anomaly detection models: The case of social computing research. Scientometrics, 103(2), 453–469.
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
Clauset, A., Larremore, D. B., & Sinatra, R. (2017). Data-driven predictions in the science of science. Science, 355(6324), 477–480.
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 066111.
Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2019). A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 27, 1–22.
DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118–121.
Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8599–8603). IEEE.
Dridi, A., Gaber, M. M., Azad, R. M. A., & Bhogal, J. (2020). Scholarly data mining: A systematic review of its applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11, e1395.
Ebadi, A., Tremblay, S., Goutte, C., & Schiffauerova, A. (2020). Application of machine learning techniques to assess the trends and alignment of the funded research output. Journal of Informetrics, 14(2), 101018.
Effendy, S., & Yap, R. H. C. (2017) 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). International World Wide Web Conferences Steering Committee.
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., et al. (2018). Science of science. Science, 359, 6379.
Garousi, V., & Ruhe, G. (2013). A bibliometric/geographic assessment of 40 years of software engineering research (1969–2009). International Journal of Software Engineering and Knowledge Engineering, 23(09), 1343–1366.
Goodall, A. H. (2006). Should top universities be led by top researchers and are they? A citations analysis. Journal of Documentation, 62(3), 388–411.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). MIT Press.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier.
Hegselmann, R., Krause, U., et al. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5, 3.
Hochreiter, S., & Schmidhuber, J. (1997) Lstm can solve hard long time lag problems. In Advances in neural information processing systems (pp. 473–479).
Hoonlor, A., Szymanski, B. K., & Zaki, M. J. (2013). Trends in computer science research. Communications of the ACM, 56(10), 74–83.
Hurtado, J. L., Agarwal, A., & Zhu, X. (2016). Topic discovery and future trend forecasting for texts. Journal of Big Data, 3(1), 7.
Jabłońska-Sabuka, M., Sitarz, R., & Kraslawski, A. (2014). Forecasting research trends using population dynamics model with burgers’ type interaction. Journal of Informetrics, 8(1), 111–122.
Katsurai, M., & Ono, S. (2019). Trendnets: Mapping emerging research trends from dynamic co-word networks via sparse representation. Scientometrics, 121(3), 1583–1598.
Krenn, M., & Zeilinger, A. (2020). Predicting research trends with semantic and neural networks with an application in quantum physics. Proceedings of the National Academy of Sciences, 117(4), 1910–1916.
Leydesdorff, L. (2001). The challenge of scientometrics: The development, measurement, and self-organization of scientific communications. Universal-Publishers.
Mahalakshmi, G. S., Selvi, G. M., & Sendhilkumar, S. (2017) A bibliometric analysis of journal of informetrics—A decade study. In 2017 Second international conference on recent trends and challenges in computational models (ICRTCCM) (pp. 222–227). IEEE.
Mandic, D., & Chambers, J. (2001). Recurrent neural networks for prediction: Learning algorithms, architectures and stability. Wiley.
Pham, M. C., Klamma, R., & Jarke, M. (2011). Development of computer science disciplines: A social network analysis approach. Social Network Analysis and Mining, 1(4), 321–340.
Poznanski, A., & Wolf, L. (2016) Cnn-n-gram for handwriting word recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2305–2314).
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.
Salatino, A. A., Osborne, F., & Motta, E. (2018) Augur: Forecasting the emergence of new research topics. In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries (pp. 303–312).
Sanderson, M., & Croft, B. (1999) Deriving concept hierarchies from text. In Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (pp. 206–213).
Sari, N., Widodo, A., et al. (2012). Trend prediction for computer science research topics using extreme learning machine. Procedia Engineering, 50, 871–881.
Shen, Z., Ma, H., & Wang, K. (2018). A web-scale system for scientific knowledge exploration. arXiv preprint arXiv:1805.12216.
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B. J., & Wang, K. (2015). An overview of microsoft academic service (mas) and applications. In Proceedings of the 24th international conference on world wide web (pp. 243–246).
Sitarz, R., & Kraslawski, A. (2012) Application of semantic and lexical analysis to technology forecasting by trend analysis-thematic clusters in separation processes. In Computer aided chemical engineering (Vol. 30, pp. 437–441). Elsevier.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). 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).
Tom, Y., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55–75.
Tseng, Y. H., Lin, Y. I., Lee, Y. Y., Hung, W. C., & Lee, C. H. (2009). A comparison of methods for detecting hot topics. Scientometrics, 81(1), 73–90.
Wang, L., & Sng, D. (2015). Deep learning algorithms with applications to video analytics for a smart city: A survey. arXiv preprint arXiv:1512.03131.
Wang, Z., Li, B., & Ma, Y. (2014) An analysis of research in software engineering: Assessment and trends. arXiv preprint arXiv:1407.4903.
Wu, Y., Venkatramanan, S., & Chiu, D. M. (2016). Research collaboration and topic trends in computer science based on top active authors. PeerJ Computer Science, 2, e41.
Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017). Big scholarly data: A survey. IEEE Transactions on Big Data, 3(1), 18–35.
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Taheri, S., Aliakbary, S. Research trend prediction in computer science publications: a deep neural network approach. Scientometrics 127, 849–869 (2022). https://doi.org/10.1007/s11192-021-04240-2