Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023. Association for Computing Machinery, Inc, 2023. S. 813-819 (Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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RIS
TY - CHAP
T1 - Deliberative Diversity for News Recommendations
T2 - 17th ACM Conference on Recommender Systems, RecSys 2023
AU - Heitz, Lucien
AU - Lischka, Juliane A.
AU - Abdullah, Rana
AU - Laugwitz, Laura
AU - Meyer, Hendrik
AU - Bernstein, Abraham
N1 - Publisher Copyright: © 2023 Owner/Author.
PY - 2023/9/14
Y1 - 2023/9/14
N2 - 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.
AB - 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.
KW - deliberative diversity
KW - journalism
KW - recommender system
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85174488304&partnerID=8YFLogxK
U2 - 10.1145/3604915.3608834
DO - 10.1145/3604915.3608834
M3 - Article in conference proceedings
AN - SCOPUS:85174488304
T3 - Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
SP - 813
EP - 819
BT - Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
PB - Association for Computing Machinery, Inc
Y2 - 18 September 2023 through 22 September 2023
ER -