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Prediction and application of article potential citations based on nonlinear citation-forecasting combined model

Author

Listed:
  • Kehan Wang

    (Zhengzhou University)

  • Wenxuan Shi

    (Zhengzhou University)

  • Junsong Bai

    (Southern Medical University)

  • Xiaoping Zhao

    (University of California. Irvine)

  • Liying Zhang

    (Zhengzhou University)

Abstract

As the number of academic articles rapidly increases, a reasonable evaluation method for the articles is highly required in the current academic research. Meanwhile, a faster access to the high-quality academic articles for the researchers is also of critical significance. This paper first improves the AVG model and presents a new Nonlinear Citation-Forecasting Combined Model (NCFCM) based on a neural network to predict the potential increase of citation counts. Then, the NCFCM is used to analyze and rank the academic articles in online databases. The results of NCFCM model are compared to the results from other existing methods. Empirical analysis and comparisons demonstrate that the NCFCM model is of high accuracy and robustness in forecasting potential citation counts and ranking academic articles. Ranking academic articles according to the potentional citation counts can help researchers retrieve the desired articles efficiently in a short time.

Suggested Citation

  • Kehan Wang & Wenxuan Shi & Junsong Bai & Xiaoping Zhao & Liying Zhang, 2021. "Prediction and application of article potential citations based on nonlinear citation-forecasting combined model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6533-6550, August.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04026-6
    DOI: 10.1007/s11192-021-04026-6
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    3. Fang Zhang & Shengli Wu, 2024. "Predicting citation impact of academic papers across research areas using multiple models and early citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4137-4166, July.

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