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End user searching on the Internet: An analysis of term pair topics submitted to the Excite search engine

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  • Nancy C. M. Ross
  • Dietmar Wolfram

Abstract

Queries submitted to the Excite search engine were analyzed for subject content based on the cooccurrence of terms within multiterm queries. More than 1000 of the most frequently cooccurring term pairs were categorized into one or more of 30 developed subject areas. Subject area frequencies and their cooccurrences with one another were tallied and analyzed using hierarchical cluster analysis and multidimensional scaling. The cluster analyses revealed several anticipated and a few unanticipated groupings of subjects, resulting in several well‐defined high‐level clusters of broad subject areas. Multidimensional scaling of subject cooccurrences revealed similar relationships among the different subject categories. Applications that arise from a better understanding of the topics users search and their relationships are discussed.

Suggested Citation

  • Nancy C. M. Ross & Dietmar Wolfram, 2000. "End user searching on the Internet: An analysis of term pair topics submitted to the Excite search engine," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(10), pages 949-958.
    • Handle: RePEc:bla:jamest:v:51:y:2000:i:10:p:949-958
    DOI: 10.1002/1097-4571(2000)51:103.0.CO;2-5
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    Cited by:

    1. Trudie Strauss & Michael Johan von Maltitz, 2017. "Generalising Ward’s Method for Use with Manhattan Distances," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    2. Judit Bar-Ilan, 2001. "Data collection methods on the Web for infometric purposes — A review and analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(1), pages 7-32, January.
    3. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.
    4. Vaughan, Liwen & You, Justin, 2010. "Word co-occurrences on Webpages as a measure of the relatedness of organizations: A new Webometrics concept," Journal of Informetrics, Elsevier, vol. 4(4), pages 483-491.

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