@inproceedings{6ad57a154da5441fa33514d9653aa6ce,
title = "What Are Filter Bubbles Really? A Review of the Conceptual and Empirical Work",
abstract = "The original filter bubble thesis states that the use of personalization algorithms results in a unique universe of information for each of us, with far-reaching individual and societal consequences. The ambiguity of the original thesis has prompted both a conceptual debate regarding its definition and has forced empirical researchers to consider their own interpretations. This has led to contrasting empirical results and minimal generalizability across studies. To reliably answer the question of whether filter bubbles exists, on what platforms, and what caused them, we need a systematically and empirically verifiable definition of the filter bubble that can be used to develop rigorous tests for the existence and strength of a filter bubble. In this paper, we propose an operationalized definition of the (technological) filter bubble and interpret previous empirical work in light of this new definition.",
keywords = "recommendation, diversity, recommender system, personalization, filter bubble",
author = "Lien Michiels and Jens Leysen and Annelien Smets and Bart Goethals",
year = "2022",
month = jul,
day = "4",
doi = "10.1145/3511047.3538028",
language = "English",
isbn = "9781450392327",
series = "UMAP '22 Adjunct",
publisher = "Association for Computing Machinery",
pages = "274–279",
booktitle = "Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization",
note = "30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP {\textquoteright}22 ; Conference date: 04-07-2022 Through 07-07-2022",
}