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The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts

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  • Babak Sohrabi

    (University of Tehran)

  • Hamideh Iraj

    (University of Tehran)

Abstract

This paper investigates an association between two new variables and citations in papers. These variables include the abstract ratio (the sum of repetition of keywords in abstract divided by abstract length) and the weight ratio (the frequency of paper’s keyword per journal). The data consist of 5875 papers from 12 journals in education: three journals from each SCImago quartile. The researchers used semi-continuous regression to model the data and measure the impact of the proposed variables on citations. The results revealed that both abstract ratio and weight ratio are statistically significant predictors of citations in scientific articles in education.

Suggested Citation

  • Babak Sohrabi & Hamideh Iraj, 2017. "The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 243-251, January.
    • Handle: RePEc:spr:scient:v:110:y:2017:i:1:d:10.1007_s11192-016-2161-5
    DOI: 10.1007/s11192-016-2161-5
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    References listed on IDEAS

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    Cited by:

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    2. Martorell Cunil, Onofre & Otero González, Luis & Durán Santomil, Pablo & Mulet Forteza, Carlos, 2023. "How to accomplish a highly cited paper in the tourism, leisure and hospitality field," Journal of Business Research, Elsevier, vol. 157(C).
    3. Bai, Xiaomei & Zhang, Fuli & Lee, Ivan, 2019. "Predicting the citations of scholarly paper," Journal of Informetrics, Elsevier, vol. 13(1), pages 407-418.
    4. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).
    5. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    6. Chowdhury, K.P., 2021. "Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
    7. Jinqing Yang & Zhifeng Liu & Xiufeng Cheng & Guanghui Ye, 2024. "Understanding the keyword adoption behavior patterns of researchers from a functional structure perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3359-3384, June.
    8. Brady D. Lund & Sanjay Kumar Maurya, 2020. "The relationship between highly-cited papers and the frequency of citations to other papers within-issue among three top information science journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2491-2504, December.
    9. Sergio Jimenez & Youlin Avila & George Dueñas & Alexander Gelbukh, 2020. "Automatic prediction of citability of scientific articles by stylometry of their titles and abstracts," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3187-3232, December.
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