An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

• Published in: IEEE access Vol. 12; pp. 41773 - 41786
• Main Authors: Qiu, Junping, Han, Xiaolin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Academic papers; Algorithms; artificial neural network; Artificial neural networks; Bibliometrics; Citation analysis; classification and regression tree; Decision making; Information science; Libraries; Machine learning; multiple linear regression; prediction model; Prediction models; Predictions; Predictive models; Research management; research value
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3378569

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.