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Research trend prediction in computer science publications: a deep neural network approach

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Abstract

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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Notes

  1. https://academic.microsoft.com/publications.

  2. aminer.org/citation.

  3. microsoft.com/en-us/research/project/microsoft-academic-graph.

  4. dblp.org.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

    Soroush Taheri & Sadegh Aliakbary

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  1. Soroush Taheri
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  2. Sadegh Aliakbary
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Correspondence to Sadegh Aliakbary.

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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

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