Publication
Title
How should we measure filter bubbles? A regression model and evidence for online news
Author
Abstract
News media play an important role in democratic societies. Central to fulfilling this role is the premise that users should be exposed to diverse news. However, news recommender systems are gaining popularity on news websites, which has sparked concerns over filter bubbles. More specifically, editors, policy-makers and scholars are worried that these news recommender systems may expose users to less diverse content over time. To the best of our knowledge, this hypothesis has not been tested in a longitudinal observational study of real users that interact with a real news website. Such observational studies require the use of research methods that are robust and can account for the many covariates that may influ- ence the diversity of recommendations at any given time. In this work, we propose an analysis model to study whether the variety of articles recommended to a user decreases over time in such an ob- servational study design. Further, we present results from two case studies using aggregated and anonymized data that were collected by two western European news websites employing a collaborative filtering-based news recommender system to serve (personalized) recommendations to their users. Through these case studies we validate empirically that our modeling assumptions are sound and supported by the data, and that our model obtains more reliable and interpretable results than analysis methods used in prior empirical work on filter bubbles. Our case studies provide evidence of a small decrease in the topic variety of a user’s recommendations in the first weeks after they sign up, but no evidence of a decrease in political variety.
Language
English
Source (book)
RecSys '23 : proceedings of the 17th ACM Conference on Recommender Systems, September 18-22, 2023, Singapore
Publication
New York, N.Y. : Association for Computing Machinery , 2023
ISBN
979-84-00-70241-9
DOI
10.1145/3604915.3608805
Volume/pages
p. 640-651
ISI
001156630300064
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 06.11.2023
Last edited 08.05.2024
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