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Interested in Diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization

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Bodó, Balázs, Helberger, Natali, Eskens, Sarah, Möller, Judith·2019·Digital Journalism
DOI 10.1080/21670811.2018.1521292
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Abstract

Using survey evidence from the Netherlands, we explore the factors that influence news readers’ attitudes toward news personalization. We show that the value of personalization depends on commonly overlooked factors, such as concerns about a shared news sphere, and the depth and diversity of recommendations. However, these expectations are not universal. Younger, less educated users have little exposure to non-personalized news, and they also show little concern about diverse news recommendations. We discuss the policy implications of our findings. We show that quality news organizations that pursue reader loyalty and trust have a strong incentive to implement personalization algorithms that help them achieve these particular goals by taking into account diversity expecting user attitudes and providing high quality recommendations. Diversity-valuing news readers are thus well placed to be served by diversity-enhancing recommender algorithms. However, some users are in danger of being left out of this positive feedback loop. We make specific policy suggestions regarding how to address the issue of diversity-reducing feedback loops, and encourage the development of diversity-enhancing ones.

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Cited by

Sorted by citations

Authors
Bodó, B., Helberger, N., Eskens, S., & Möller, J.
Affiliations
University of Amsterdam (NL)
Corresponding author
Bodó, Balázs
ORCID
Venue
Digital Journalism
Publisher
Informa UK Limited
Published
2018-12-17
Volume
7
Issue
2
Pages
206-229
ISSN
  • 2167-0811 (ISSN-L)
  • 2167-082X
DOI
10.1080/21670811.2018.1521292
OpenAlex
W2904849632
Cited by
141
References
7
Citations by year
2026: 15 · 2025: 26 · 2024: 24 · 2023: 18 · 2022: 20 · 2021: 22 · 2020: 12 · 2019: 4
FWCI
14.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Digital Journalism (publishedVersion, cc-by)
  • UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
PERSONEWS ERC-2014-STG, European Research Council (638514)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Sociology and Political Science › Media Influence and Politics
  • Social Sciences › Communication › Media Studies and Communication
Keywords
personalized news, recommendation algorithms, news quality, reader loyalty, news policy

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