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Webometric research with the Bing Search API 2.0

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  • Thelwall, Mike
  • Sud, Pardeep

Abstract

In May 2011 the Bing Search API 2.0 had become the only major international web search engine data source available for automatic offline processing for webometric research. This article describes its key features, contrasting them with previous web search data sources, and discussing implications for webometric research. Overall, it seems that large-scale quantitative web research is possible with the Bing Search API 2.0, including query splitting, but that legal issues require the redesign of webometric software to ensure that all results obtained from Bing are displayed directly to the user.

Suggested Citation

  • Thelwall, Mike & Sud, Pardeep, 2012. "Webometric research with the Bing Search API 2.0," Journal of Informetrics, Elsevier, vol. 6(1), pages 44-52.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:1:p:44-52
    DOI: 10.1016/j.joi.2011.10.002
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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. Mike Thelwall, 2008. "Quantitative comparisons of search engine results," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(11), pages 1702-1710, September.
    3. Judit Bar‐Ilan & Bluma C. Peritz, 2009. "A method for measuring the evolution of a topic on the Web: The case of “informetrics”," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(9), pages 1730-1740, September.
    4. Mark Truran & Jan‐Felix Schmakeit & Helen Ashman, 2011. "The effect of user intent on the stability of search engine results," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1276-1287, July.
    5. 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.
    6. Mark Truran & Jan-Felix Schmakeit & Helen Ashman, 2011. "The effect of user intent on the stability of search engine results," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1276-1287, July.
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    Citations

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    Cited by:

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    5. Judit Bar-Ilan & Rina Azoulay, 2012. "Map of nonprofit organization websites in Israel," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1142-1167, June.
    6. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.
    7. Amalia Mas-Bleda & Mike Thelwall, 2016. "Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2007-2030, December.
    8. Pardeep Sud & Mike Thelwall, 2014. "Linked title mentions: a new automated link search candidate," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1831-1849, December.

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