How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News

Lien Michiels, Jorre Vannieuwenhuyze, Jens Leysen, Robin Verachtert, Annelien Smets, Bart Goethals

Research output: Chapter in Book/Report/Conference proceedingConference paper

5 Citations (Scopus)

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 influence 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 observational 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.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on Recommender Systems
PublisherACM Digital Library
Pages640–651
Number of pages12
ISBN (Electronic)9798400702419
DOIs
Publication statusPublished - 14 Sep 2023
Event17th ACM Conference on Recommender Systems
- , Singapore
Duration: 18 Sep 202322 Sep 2023

Publication series

NameProceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023

Conference

Conference17th ACM Conference on Recommender Systems
Country/TerritorySingapore
Period18/09/2322/09/23

Bibliographical note

Funding Information:
This research was partially supported by the Research Foundation Flanders (FWO) under grant number S006323N and also received funding from the Flemish Government (AI Research Program).

Publisher Copyright:
© 2023 ACM.

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