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
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Notes
- 1.
Numbers for market share as per 2018 market research reported in https://www.smartinsights.com/search-engine-marketing/search-engine-statistics/.
References
Adomavicius, G., Tuzhilin, A.: Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83–90 (2005)
Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)
Broder, A., et al.: Graph structure in the web. Comput. Netw. 33(1–6), 309–320 (2000)
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)
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)
Ebrahim, Z., Irani, Z.: E-government adoption: architecture and barriers. Bus. Process. Manag. J. 11(5), 589–611 (2005)
Foster, R.: News plurality in a digital world. Reuters Institute for the Study of Journalism Oxford (2012)
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
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)
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)
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)
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)
Henningsson, S., van Veenstra, A.F.: Barriers to it-driven governmental transformation. In: ECIS, p. 113 (2010)
Hölscher, C., Strube, G.: Web search behavior of internet experts and newbies. Comput. Netw. 33(1–6), 337–346 (2000)
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)
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)
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)
Official Google blog: Personalized search for everyone (2009). https://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html. Accessed 27 Apr 2019
Ørmen, J.: Googling the news: opportunities and challenges in studying news events through Google Search. Digital J. 4(1), 107–124 (2016)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
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)
Pariser, E.: The filter bubble: what the Internet is hiding from you, Penguin, UK (2011)
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)
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)
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)
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)
Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (TOIS) 28(4), 20 (2010)
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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
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