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.

中文翻译：

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你看见什么了？一项用于衡量Google搜索引擎中的个性化设置的研究

在本文中，我们介绍了“ #Datenspende”项目的结果，在该项目中，2017年德国大选期间，有4000多人对与德国大选相关的关键字进行了搜索。搜索结果的个性化很小。因此，Eli Pariser的过滤器气泡理论中提到的效应在此数据中出现的机会也很小，可以忽略不计。我们通过对捐赠的结果列表应用各种相似性度量来获得这些结果。第一种使用共同结果的数量作为相似性度量的方法显示，个性化空间在搜索人时平均少于十个结果中的两个，而在搜索方时则最多四个。其他更具体的措施的应用表明该空间的确较小，因此滤泡的存在并不明显。此外，该项目也是一个概念证明，因为它使社会能够永久监控搜索引擎的个性化程度，以确保搜索引擎的个性化程度。任何所需的搜索词。如果适当的API限制了对与研究相关的内容的选择性访问，以建立相似的可信度，那么一般设计也可以转移到中介机构。