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Automatic classification of academic web page types

Author

Listed:
  • Patrick Kenekayoro

    (University of Wolverhampton)

  • Kevan Buckley

    (University of Wolverhampton)

  • Mike Thelwall

    (University of Wolverhampton)

Abstract

Counts of hyperlinks between websites can be unreliable for webometrics studies so researchers have attempted to find alternate counting methods or have tried to identify the reasons why links in websites are created. Manual classification of individual links in websites is infeasible for large webometrics studies, so a more efficient approach to identifying the reasons for link creation is needed to fully harness the potential of hyperlinks for webometrics research. This paper describes a machine learning method to automatically classify hyperlink source and target page types in university websites. 78 % accuracy was achieved for automatically classifying web page types and up to 74 % accuracy for predicting link target page types from link source page characteristics.

Suggested Citation

  • Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2014. "Automatic classification of academic web page types," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1015-1026, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1292-9
    DOI: 10.1007/s11192-014-1292-9
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    References listed on IDEAS

    as
    1. Mike Thelwall, 2001. "Extracting macroscopic information from Web links," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(13), pages 1157-1168.
    2. Mike Thelwall, 2006. "Interpreting social science link analysis research: A theoretical framework," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(1), pages 60-68, January.
    3. Judit Bar-Ilan, 2004. "A microscopic link analysis of academic institutions within a country — the case of Israel," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 391-403, March.
    4. Liwen Vaughan & Guozhu Wu, 2004. "Links to commercial websites as a source of business information," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 487-496, August.
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    Cited by:

    1. Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2015. "Clustering research group website homepages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2023-2039, March.
    2. Patrick Kenekayoro, 2018. "Identifying named entities in academic biographies with supervised learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 751-765, August.
    3. Florian Kreuchauff & Vladimir Korzinov, 2017. "A patent search strategy based on machine learning for the emerging field of service robotics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 743-772, May.

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