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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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    6. Ale Ebrahim, Nader & Salehi, Hadi & Embi, Mohamed Amin & Habibi Tanha, Farid & Gholizadeh, Hossein & Motahar, Seyed Mohammad & Ordi, Ali, 2013. "Effective Strategies for Increasing Citation Frequency," MPRA Paper 50919, University Library of Munich, Germany, revised 12 Oct 2013.
    7. Fatemeh Rostami & Asghar Mohammadpoorasl & Mohammad Hajizadeh, 2014. "The effect of characteristics of title on citation rates of articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2007-2010, March.
    8. Bornmann, Lutz & Schier, Hermann & Marx, Werner & Daniel, Hans-Dieter, 2012. "What factors determine citation counts of publications in chemistry besides their quality?," Journal of Informetrics, Elsevier, vol. 6(1), pages 11-18.
    9. Lawrence D. Fu & Constantin F. Aliferis, 2010. "Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 257-270, October.
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    Cited by:

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    4. 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.
    5. 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).
    6. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.
    7. 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).
    8. Bai, Xiaomei & Zhang, Fuli & Lee, Ivan, 2019. "Predicting the citations of scholarly paper," Journal of Informetrics, Elsevier, vol. 13(1), pages 407-418.
    9. 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.
    10. 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).
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