Skip to main content

You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11864)

Abstract

The influence of Web search personalisation on professional knowledge work is an understudied area. Here we investigate how public sector officials self-assess their dependency on the Google Web search engine, whether they are aware of the potential impact of algorithmic biases on their ability to retrieve all relevant information, and how much relevant information may actually be missed due to Web search personalisation. We find that the majority of participants in our experimental study are neither aware that there is a potential problem nor do they have a strategy to mitigate the risk of missing relevant information when performing online searches. Most significantly, we provide empirical evidence that up to \(20\%\) of relevant information may be missed due to Web search personalisation. This work has significant implications for Web research by public sector professionals, who should be provided with training about the potential algorithmic biases that may affect their judgments and decision making, as well as clear guidelines how to minimise the risk of missing relevant information.

Keywords

  • Web search
  • Personalisation
  • Human-computer interaction
  • Social informatics

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-34971-4_17
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-34971-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Notes

  1. 1.

    Numbers for market share as per 2018 market research reported in https://www.smartinsights.com/search-engine-marketing/search-engine-statistics/.

References

  1. Adomavicius, G., Tuzhilin, A.: Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83–90 (2005)

    CrossRef  Google Scholar 

  2. Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)

    CrossRef  Google Scholar 

  3. Broder, A., et al.: Graph structure in the web. Comput. Netw. 33(1–6), 309–320 (2000)

    CrossRef  Google Scholar 

  4. Du, J.T., Evans, N.: Academic users’ information searching on research topics: characteristics of research tasks and search strategies. J. Acad. Libr. 37(4), 299–306 (2011)

    CrossRef  Google Scholar 

  5. Dutton, W.H., Reisdorf, B., Dubois, E., Blank, G.: Search and politics: the uses and impacts of search in Britain, France, Germany, Italy, Poland, Spain, and the United States (2017)

    Google Scholar 

  6. Ebrahim, Z., Irani, Z.: E-government adoption: architecture and barriers. Bus. Process. Manag. J. 11(5), 589–611 (2005)

    CrossRef  Google Scholar 

  7. Foster, R.: News plurality in a digital world. Reuters Institute for the Study of Journalism Oxford (2012)

    Google Scholar 

  8. Google news blog: personalized search graduates from Google labs (2005). https://googlepress.blogspot.com/2005/11/personalized-search-graduates-from_10.html. Accessed 27 Apr 2019

  9. Granka, L.A., Joachims, T., Gay, G.: Eye-tracking analysis of user behavior in WWW search. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 478–479. ACM (2004)

    Google Scholar 

  10. Haim, M., Graefe, A., Brosius, H.B.: Burst of the filter bubble? Effects of personalization on the diversity of Google news. Digital J. 6(3), 330–343 (2018)

    CrossRef  Google Scholar 

  11. Hannak, A., et al.: Measuring personalization of web search. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 527–538. ACM (2013)

    Google Scholar 

  12. van Hardeveld, G.J., Webber, C., O’Hara, K.: Deviating from the cybercriminal script: exploring tools of anonymity (Mis) used by carders on cryptomarkets. Am. Behav. Sci. 61(11), 1244–1266 (2017)

    CrossRef  Google Scholar 

  13. Henningsson, S., van Veenstra, A.F.: Barriers to it-driven governmental transformation. In: ECIS, p. 113 (2010)

    Google Scholar 

  14. Hölscher, C., Strube, G.: Web search behavior of internet experts and newbies. Comput. Netw. 33(1–6), 337–346 (2000)

    CrossRef  Google Scholar 

  15. Lan, Z., Cayer, N.J.: The challenges of teaching information technology use and management in a time of information revolution. Am. Rev. Public Adm. 24(2), 207–222 (1994)

    CrossRef  Google Scholar 

  16. Lewandowski, D.: Evaluating the retrieval effectiveness of web search engines using a representative query sample. J. Assoc. Inf. Sci. Technol. 66(9), 1763–1775 (2015)

    CrossRef  Google Scholar 

  17. Lu, X., Moffat, A., Culpepper, J.S.: The effect of pooling and evaluation depth on IR metrics. Inf. Retr. J. 19(4), 416–445 (2016)

    CrossRef  Google Scholar 

  18. Official Google blog: Personalized search for everyone (2009). https://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html. Accessed 27 Apr 2019

  19. Ørmen, J.: Googling the news: opportunities and challenges in studying news events through Google Search. Digital J. 4(1), 107–124 (2016)

    CrossRef  Google Scholar 

  20. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  21. Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., Granka, L.: In google we trust: Users’ decisions on rank, position, and relevance. J. Comput.-Mediat. Commun. 12(3), 801–823 (2007)

    CrossRef  Google Scholar 

  22. Pariser, E.: The filter bubble: what the Internet is hiding from you, Penguin, UK (2011)

    Google Scholar 

  23. Robertson, R.E., Lazer, D., Wilson, C.: Auditing the personalization and composition of politically-related search engine results pages. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 955–965. International World Wide Web Conferences Steering Committee (2018)

    Google Scholar 

  24. Roesner, F., Kohno, T., Wetherall, D.: Detecting and defending against third-party tracking on the web. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p. 12. USENIX Association (2012)

    Google Scholar 

  25. Salehi, S., Du, J.T., Ashman, H.: Examining personalization in academic web search. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp. 103–111. ACM (2015)

    Google Scholar 

  26. Savoldelli, A., Codagnone, C., Misuraca, G.: Understanding the e-government paradox: learning from literature and practice on barriers to adoption. Gov. Inf. Q. 31, S63–S71 (2014)

    CrossRef  Google Scholar 

  27. Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (TOIS) 28(4), 20 (2010)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Markus Luczak-Roesch .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lai, C., Luczak-Roesch, M. (2019). You Can’t See What You Can’t See: Experimental Evidence for How Much Relevant Information May Be Missed Due to Google’s Web Search Personalisation. In: , et al. Social Informatics. SocInfo 2019. Lecture Notes in Computer Science(), vol 11864. Springer, Cham. https://doi.org/10.1007/978-3-030-34971-4_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34971-4_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34970-7

  • Online ISBN: 978-3-030-34971-4

  • eBook Packages: Computer ScienceComputer Science (R0)