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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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    7. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
    8. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
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    Cited by:

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    3. 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).
    4. Bai, Xiaomei & Zhang, Fuli & Lee, Ivan, 2019. "Predicting the citations of scholarly paper," Journal of Informetrics, Elsevier, vol. 13(1), pages 407-418.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. Yangping Zhou, 2021. "Self-citation and citation of top journal publishers and their interpretation in the journal-discipline context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6013-6040, July.
    10. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
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