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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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    1. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2014. "How to improve the prediction based on citation impact percentiles for years shortly after the publication date?," Journal of Informetrics, Elsevier, vol. 8(1), pages 175-180.
    2. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    3. David Guy Brizan & Kevin Gallagher & Arnab Jahangir & Theodore Brown, 2016. "Predicting citation patterns: defining and determining influence," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 183-200, July.
    4. 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.
    5. 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.
    6. 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.
    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.
    9. 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.
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    7. 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.
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