Abstract
Search engines and social media keep trace of profile- and behavioral-based distinct signals of their users, to provide them personalized and recommended content. Here, we focus on the level of web search personalization, to estimate the risk of trapping the user into so called Filter Bubbles. Our experimentation has been carried out on news, specifically investigating the Google News platform. Our results are in line with existing literature and call for further analyses on which kind of users are the target of specific recommendations by Google.
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Cozza, V., Hoang, V. T., Petrocchi, M., & Spognardi, A. (2016). Experimental measures of news personalization in Google News. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9881 LNCS, pp. 93–104). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-46963-8_8
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