Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

  • Lucien Heitz
  • Juliane A. Lischka
  • Rana Abdullah
  • Laura Laugwitz
  • Hendrik Meyer
  • Abraham Bernstein

News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under scrutiny. In the area of personalized news, many platforms make design choices driven by economic incentives. In contrast to such systems that optimize for financial gain, there can be norm-driven diversity systems that prioritize normative and democratic goals. However, their impact on users in terms of inducing behavioral change or influencing knowledge is still understudied. In this paper, we contribute to the field of news recommender system design by conducting a user study that examines the impact of these normative approaches. We a.) operationalize the notion of a deliberative public sphere for news recommendations, show b.) the impact on news usage, and c.) the influence on political knowledge, attitudes and voting behavior. We find that exposure to small parties is associated with an increase in knowledge about their candidates and that intensive news consumption about a party can change the direction of attitudes of readers towards the issues of the party.

OriginalspracheEnglisch
TitelProceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
Anzahl der Seiten7
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum14.09.2023
Seiten813-819
ISBN (elektronisch)9798400702419
DOIs
PublikationsstatusErschienen - 14.09.2023
Extern publiziertJa
Veranstaltung17th ACM Conference on Recommender Systems, RecSys 2023 - Singapore, Singapur
Dauer: 18.09.202322.09.2023

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DOI