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Interpreting social science link analysis research: A theoretical framework

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  • Mike Thelwall

Abstract

Link analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of links and of the Web. A critical but very difficult issue is how to interpret the results of social science link analyses. It is argued that the dynamic nature of the Web, its lack of quality control, and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the Web makes application of qualitative methodologies and pure reason very problematic to large‐scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the Web is incapable of giving definitive answers to large‐scale link analysis research questions concerning social factors underlying link creation. Finally, it is claimed that although theoretical frameworks are appropriate for guiding research, a Theory of Link Analysis is not possible.

Suggested Citation

  • Mike Thelwall, 2006. "Interpreting social science link analysis research: A theoretical framework," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(1), pages 60-68, January.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:1:p:60-68
    DOI: 10.1002/asi.20253
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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. Giada Baldessarelli & Nathalie Lazaric & Michele Pezzoni, 2022. "Organizational routines: Evolution in the research landscape of two core communities," Post-Print halshs-03718851, HAL.
    3. 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.
    4. George Masterton & Erik J. Olsson & Staffan Angere, 2016. "Linking as voting: how the Condorcet jury theorem in political science is relevant to webometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 945-966, March.
    5. Judit Bar-Ilan & Mark Levene, 2015. "The hw-rank: an h-index variant for ranking web pages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2247-2253, March.
    6. 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.
    7. Enrique Orduña-Malea & Adolfo Alonso-Arroyo & José-Antonio Ontalba-Ruipérez & Ferrán Catalá-López, 2023. "Evaluating the online impact of reporting guidelines for randomised trial reports and protocols: a cross-sectional web-based data analysis of CONSORT and SPIRIT initiatives," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 407-440, January.
    8. Mike Thelwall, 2016. "Interpreting correlations between citation counts and other indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 337-347, July.
    9. Frank Bakker & Iina Hellsten, 2013. "Capturing Online Presence: Hyperlinks and Semantic Networks in Activist Group Websites on Corporate Social Responsibility," Journal of Business Ethics, Springer, vol. 118(4), pages 807-823, December.
    10. Enrique Orduña-Malea & Rodrigo Costas, 2021. "Link-based approach to study scientific software usage: the case of VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8153-8186, September.
    11. Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2014. "Automatic classification of academic web page types," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1015-1026, November.
    12. Patrick Kenekayoro & Kevan Buckley & Mike Thelwall, 2015. "Clustering research group website homepages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2023-2039, March.
    13. Kayvan Kousha & Mike Thelwall, 2008. "Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 74(2), pages 273-294, February.
    14. José-Antonio Ontalba-Ruipérez & Enrique Orduna-Malea & Adolfo Alonso-Arroyo, 2016. "Identifying institutional relationships in a geographically distributed public health system using interlinking and co-authorship methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1167-1191, March.
    15. 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.
    16. Giada Baldessarelli & Nathalie Lazaric & Michele Pezzoni, 2022. "Organizational routines: Evolution in the research landscape of two core communities," Journal of Evolutionary Economics, Springer, vol. 32(4), pages 1119-1154, September.
    17. 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.
    18. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    19. Kim Holmberg, 2010. "Co-inlinking to a municipal Web space: a webometric and content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 851-862, June.
    20. Jose Luis Ortega & Isidro Aguillo & Viv Cothey & Andrea Scharnhorst, 2008. "Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 74(2), pages 295-308, February.

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