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Word co-occurrences on Webpages as a measure of the relatedness of organizations: A new Webometrics concept

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  • Vaughan, Liwen
  • You, Justin

Abstract

Web hyperlink analysis has been a key topic of Webometric research. However, inlink data collection from commercial search engines has been limited to only one source in recent years, which is not a promising prospect for the future development of the field. We need to tap into other Web data sources and to develop new methods. Toward this end, we propose a new Webometrics concept that is based on words rather than inlinks on Webpages. We propose that word co-occurrences on Webpages can be a measure of the relatedness of organizations. Word co-occurrence data can be collected from both general search engines and blog search engines, which expands data sources greatly. The proposed concept is tested in a group of companies in the LTE and WiMax sectors of the telecommunications industry. Data on the co-occurrences of company names on Webpages were collected from Google and Google Blog. The co-occurrence matrices were analyzed using MDS. The resulting MDS maps were compared with industry reality and with the MDS maps from co-link analysis. Results show that Web co-word analysis could potentially be as useful as Web co-link analysis. Google Blog seems to be a better source than Google for co-word data collection.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:4:y:2010:i:4:p:483-491
    DOI: 10.1016/j.joi.2010.04.005
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    References listed on IDEAS

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    1. Mike Thelwall, 2008. "Extracting accurate and complete results from search engines: Case study windows live," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 38-50, January.
    2. 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.
    3. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
    4. Liwen Vaughan & Justin You, 2008. "Content assisted web co-link analysis for competitive intelligence," Scientometrics, Springer;Akadémiai Kiadó, vol. 77(3), pages 433-444, December.
    5. Liwen Vaughan, 2006. "Visualizing linguistic and cultural differences using Web co‐link data," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(9), pages 1178-1193, July.
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    Cited by:

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    2. Thelwall, Mike & Sud, Pardeep, 2012. "Webometric research with the Bing Search API 2.0," Journal of Informetrics, Elsevier, vol. 6(1), pages 44-52.
    3. Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2015. "Clustering research group website homepages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2023-2039, March.
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    7. Krzysztof Janc, 2015. "Visibility and Connections among Cities in Digital Space," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(4), pages 3-21, October.

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