Understanding the Effects of Personalized Recommender Systems on Political News Perceptions: A Comparison of Content-Based, Collaborative, and Editorial Choice-Based News Recommender System

With the increasing implementation of algorithms across various news platforms, understanding news consumers' subjective perceptions of algorithmic-based news recommender systems has become critical. A between-subjects experiment (News Recommender System type: content-based filtering vs. collab...

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Vydáno v:Journal of broadcasting & electronic media Ročník 67; číslo 3; s. 294 - 322
Hlavní autor: Liao, Mengqi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Routledge 27.05.2023
Routledge, Taylor & Francis Group
Témata:
Algorithms
Between-subjects design
Collaboration
Consumers
Filtration
Heuristic
News
Perceptions
Recommender systems
ISSN:0883-8151, 1550-6878
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Shrnutí:With the increasing implementation of algorithms across various news platforms, understanding news consumers' subjective perceptions of algorithmic-based news recommender systems has become critical. A between-subjects experiment (News Recommender System type: content-based filtering vs. collaborative filtering vs. human editorial choice-based recommender system) with 161 participants revealed that participants tended to trust the collaborative filtering system and perceive news recommended by the system to be more credible and less biased compared to editorial choices-based or content-based recommender systems - due to the triggering of the homophily heuristic - even though the three systems recommended the same set of news. Implications were discussed.
Bibliografie:ObjectType-Article-1
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ISSN:0883-8151
1550-6878
DOI:10.1080/08838151.2023.2206662