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Thus the opportunity for the effect mentioned in Eli Pariser\u2019s filter bubble theory to occur in this data is also very small, to a degree that it is negligible. We achieved these results by applying various similarity measures to the result lists that were donated. The first approach using the number of common results as a similarity measure showed that the space for personalization is less than two results out of ten on average when searching for persons and at most four regarding the search for parties. Application of other, more specific measures show that the space is indeed smaller, so that the presence of filter bubbles is not evident.<\/jats:p>Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine\u2019s degree of personalization for any desired search terms. The general design can also be transferred to intermediaries, if appropriate APIs restrict selective access to contents relevant to the study in order to establish a similar degree of trustworthiness.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-019-0217-5","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T18:09:20Z","timestamp":1576519760000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["What did you see? 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