What did you see? A study to measure personalization in Google’s search engine
Abstract In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign. Analyzing the donated result lists we prove, that the room for per...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-12-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-019-0217-5 |
| _version_ | 1819078167685496832 |
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| author | Tobias D. Krafft Michael Gamer Katharina A. Zweig |
| author_facet | Tobias D. Krafft Michael Gamer Katharina A. Zweig |
| author_sort | Tobias D. Krafft |
| collection | DOAJ |
| description | Abstract In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign. Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser’s 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. Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine’s 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. |
| first_indexed | 2024-12-21T19:08:48Z |
| format | Article |
| id | doaj.art-d21b8f1ee0954099824cf062cbb800a5 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-21T19:08:48Z |
| publishDate | 2019-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-d21b8f1ee0954099824cf062cbb800a52022-12-21T18:53:16ZengSpringerOpenEPJ Data Science2193-11272019-12-018112310.1140/epjds/s13688-019-0217-5What did you see? A study to measure personalization in Google’s search engineTobias D. Krafft0Michael Gamer1Katharina A. Zweig2Algorithm Accountability Lab, Department of Computer Science, Technische Universität KaiserslauternAlgorithm Accountability Lab, Department of Computer Science, Technische Universität KaiserslauternAlgorithm Accountability Lab, Department of Computer Science, Technische Universität KaiserslauternAbstract In this paper we present the results of the project “#Datenspende” where during the German election in 2017 more than 4000 people contributed their search results regarding keywords connected to the German election campaign. Analyzing the donated result lists we prove, that the room for personalization of the search results is very small. Thus the opportunity for the effect mentioned in Eli Pariser’s 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. Moreover this project is also a proof of concept, as it enables society to permanently monitor a search engine’s 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.https://doi.org/10.1140/epjds/s13688-019-0217-5Filter bubbleBlack boxPersonalizationData donationSearch engineGoogle |
| spellingShingle | Tobias D. Krafft Michael Gamer Katharina A. Zweig What did you see? A study to measure personalization in Google’s search engine EPJ Data Science Filter bubble Black box Personalization Data donation Search engine |
| title | What did you see? A study to measure personalization in Google’s search engine |
| title_full | What did you see? A study to measure personalization in Google’s search engine |
| title_fullStr | What did you see? A study to measure personalization in Google’s search engine |
| title_full_unstemmed | What did you see? A study to measure personalization in Google’s search engine |
| title_short | What did you see? A study to measure personalization in Google’s search engine |
| title_sort | what did you see a study to measure personalization in google s search engine |
| topic | Filter bubble Black box Personalization Data donation Search engine |
| url | https://doi.org/10.1140/epjds/s13688-019-0217-5 |
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