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A comparison of methods for collecting web citation data for academic organizations

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  • Mike Thelwall
  • Pardeep Sud

Abstract

The primary webometric method for estimating the online impact of an organization is to count links to its website. Link counts have been available from commercial search engines for over a decade but this was set to end by early 2012 and so a replacement is needed. This article compares link counts to two alternative methods: URL citations and organization title mentions. New variations of these methods are also introduced. The three methods are compared against each other using Yahoo!. Two of the three methods (URL citations and organization title mentions) are also compared against each other using Bing. Evidence from a case study of 131 UK universities and 49 US Library and Information Science (LIS) departments suggests that Bing's Hit Count Estimates (HCEs) for popular title searches are not useful for webometric research but that Yahoo!'s HCEs for all three types of search and Bing's URL citation HCEs seem to be consistent. For exact URL counts the results of all three methods in Yahoo! and both methods in Bing are also consistent. Four types of accuracy factors are also introduced and defined: search engine coverage, search engine retrieval variation, search engine retrieval anomalies, and query polysemy.

Suggested Citation

  • Mike Thelwall & Pardeep Sud, 2011. "A comparison of methods for collecting web citation data for academic organizations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(8), pages 1488-1497, August.
  • Handle: RePEc:bla:jamist:v:62:y:2011:i:8:p:1488-1497
    DOI: 10.1002/asi.21571
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    Cited by:

    1. Enrique Orduña-Malea, 2021. "Dot-science top level domain: Academic websites or dumpsites?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3565-3591, April.
    2. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    3. Wenqiang Fan, 2015. "Contribution of the institutional repositories of the Chinese Academy of Sciences to the webometric indicators of their home institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1889-1909, December.
    4. 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.
    5. Benedetto Lepori & Isidro F. Aguillo & Marco Seeber, 2014. "Size of web domains and interlinking behavior of higher education institutions in Europe," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 497-518, August.
    6. Lepori, Benedetto & Barberio, Vitaliano & Seeber, Marco & Aguillo, Isidro, 2013. "Core–periphery structures in national higher education systems. A cross-country analysis using interlinking data," Journal of Informetrics, Elsevier, vol. 7(3), pages 622-634.
    7. Srijana Acharya & Han Woo Park, 2017. "Open data in Nepal: a webometric network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1027-1043, May.
    8. Amalia Mas-Bleda & Mike Thelwall & Kayvan Kousha & Isidro F. Aguillo, 2014. "Do highly cited researchers successfully use the social web?," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 337-356, October.
    9. Junwei Ma & Jianhua Wang & Philip Szmedra, 2019. "Sustainable Competitive Position of Mobile Communication Companies: Comprehensive Perspectives of Insiders and Outsiders," Sustainability, MDPI, vol. 11(7), pages 1-15, April.

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