Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
Number of pages7
PublisherAssociation for Computing Machinery, Inc
Publication date14.09.2023
Pages813-819
ISBN (electronic)9798400702419
DOIs
Publication statusPublished - 14.09.2023
Externally publishedYes
Event17th ACM Conference on Recommender Systems, RecSys 2023 - Singapore, Singapore
Duration: 18.09.202322.09.2023

Bibliographical note

Publisher Copyright:
© 2023 Owner/Author.

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