Understanding the Effects of Personalized Recommender Systems on Political News Perceptions: A Comparison of Content-Based, Collaborative, and Editorial Choice-Based News Recommender System
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Abstract
ABSTRACT 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.
期刊介绍：
Published quarterly for the Broadcast Education Association, the Journal of Broadcasting & Electronic Media contains timely articles about new developments, trends, and research in electronic media written by academicians, researchers, and other electronic media professionals. The Journal invites submissions of original research that examine a broad range of issues concerning the electronic media, including the historical, technological, economic, legal, policy, cultural, social, and psychological dimensions. Scholarship that extends a historiography, tests theory, or that fosters innovative perspectives on topics of importance to the field, is particularly encouraged. The Journal is open to a diversity of theoretic paradigms and methodologies.
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