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Identification of important citations by exploiting research articles’ metadata and cue-terms from content

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  • Faiza Qayyum

    (Capital University of Science and Technology)

  • Muhammad Tanvir Afzal

    (Capital University of Science and Technology)

Abstract

Citations play a pivotal role in indicating various aspects of scientific literature. Quantitative citation analysis approaches have been used over the decades to measure the impact factor of journals, to rank researchers or institutions, to discover evolving research topics etc. Researchers doubted the pure quantitative citation analysis approaches and argued that all citations are not equally important; citation reasons must be considered while counting. In the recent past, researchers have focused on identifying important citation reasons by classifying them into important and non-important classes rather than individually classifying each reason. Most of contemporary citation classification techniques either rely on full content of articles, or they are dominated by content based features. However, most of the time content is not freely available as various journal publishers do not provide open access to articles. This paper presents a binary citation classification scheme, which is dominated by metadata based parameters. The study demonstrates the significance of metadata and content based parameters in varying scenarios. The experiments are performed on two annotated data sets, which are evaluated by employing SVM, KLR, Random Forest machine learning classifiers. The results are compared with the contemporary study that has performed similar classification employing rich list of content-based features. The results of comparisons revealed that the proposed model has attained improved value of precision (i.e., 0.68) just by relying on freely available metadata. We claim that the proposed approach can serve as the best alternative in the scenarios wherein content in unavailable.

Suggested Citation

  • Faiza Qayyum & Muhammad Tanvir Afzal, 2019. "Identification of important citations by exploiting research articles’ metadata and cue-terms from content," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 21-43, January.
  • Handle: RePEc:spr:scient:v:118:y:2019:i:1:d:10.1007_s11192-018-2961-x
    DOI: 10.1007/s11192-018-2961-x
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    2. Xin An & Xin Sun & Shuo Xu, 2022. "Important citations identification with semi-supervised classification model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6533-6555, November.
    3. Faiza Qayyum & Harun Jamil & Naeem Iqbal & DoHyeun Kim & Muhammad Tanvir Afzal, 2022. "Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6471-6499, November.
    4. Xiaorui Jiang & Jingqiang Chen, 2023. "Contextualised segment-wise citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5117-5158, September.
    5. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    6. Imran Ihsan & M. Abdul Qadir, 2021. "An NLP-based citation reason analysis using CCRO," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4769-4791, June.
    7. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.
    8. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.

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