Deliberative diversity for news recommendations:operationalization and experimental user study.

Link:
Autor/in:
Beteiligte Personen:
  • Zhang, Jie
  • Chen, Li
  • Berkovsky, Shlomo
  • Zhang, Min
  • du Noia, Tommaso
  • Basilico, Justin
  • Pizzato, Luiz
  • Song, Yang
Verlag/Körperschaft:
Association for Computing Machinery (ACM)
Erscheinungsjahr:
2023
Medientyp:
Text
Schlagworte:
  • deliberative diversity
  • journalism
  • recommender system
Beschreibung:
  • 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.
Lizenz:
  • info:eu-repo/semantics/openAccess
Quellsystem:
Forschungsinformationssystem der UHH

Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/775f17da-201e-4532-ad46-a59980cb4379