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Quantitative comparisons of search engine results

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  • Mike Thelwall

Abstract

Search engines are normally used to find information or Web sites, but Webometric investigations use them for quantitative data such as the number of pages matching a query and the international spread of those pages. For this type of application, the accuracy of the hit count estimates and range of URLs in the full results are important. Here, we compare the applications programming interfaces of Google, Yahoo!, and Live Search for 1,587 single word searches. The hit count estimates were broadly consistent but with Yahoo! and Google, reporting 5–6 times more hits than Live Search. Yahoo! tended to return slightly more matching URLs than Google, with Live Search returning significantly fewer. Yahoo!'s result URLs included a significantly wider range of domains and sites than the other two, and there was little consistency between the three engines in the number of different domains. In contrast, the three engines were reasonably consistent in the number of different top‐level domains represented in the result URLs, although Yahoo! tended to return the most. In conclusion, quantitative results from the three search engines are mostly consistent but with unexpected types of inconsistency that users should be aware of. Google is recommended for hit count estimates but Yahoo! is recommended for all other Webometric purposes.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jamist:v:59:y:2008:i:11:p:1702-1710
    DOI: 10.1002/asi.20834
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    Cited by:

    1. Ping Liu & Qiong Wu & Xiangming Mu & Kaipeng Yu & Yiting Guo, 2015. "Detecting the intellectual structure of library and information science based on formal concept analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 737-762, September.
    2. Valentina Della Corte & Giovanna Del Gaudio & Fabiana Sepe & Fabiana Sciarelli, 2019. "Sustainable Tourism in the Open Innovation Realm: A Bibliometric Analysis," Sustainability, MDPI, vol. 11(21), pages 1-18, November.
    3. Han Park, 2012. "Examining academic Internet use using a combined method," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(1), pages 251-266, January.
    4. Thelwall, Mike & Sud, Pardeep, 2012. "Webometric research with the Bing Search API 2.0," Journal of Informetrics, Elsevier, vol. 6(1), pages 44-52.
    5. Muhammad Omar & Arif Mehmood & Gyu Sang Choi & Han Woo Park, 2017. "Global mapping of artificial intelligence in Google and Google Scholar," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1269-1305, December.
    6. Antal Bosch & Toine Bogers & Maurice Kunder, 2016. "Estimating search engine index size variability: a 9-year longitudinal study," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 839-856, May.
    7. Will Serrano, 2018. "Neural Networks in Big Data and Web Search," Data, MDPI, vol. 4(1), pages 1-41, December.

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