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.
Acknowledgments
The author would like to thank Dr. Kevin Munger and Ryan Tan for their feedback on the paper.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1 https://www.allsides.com/unbiased-balanced-news is a news organization that helped identify media bias in various news sources and the political bias in popular news articles.
2 Supplementary material I: https://osf.io/nhjur?view_only=016729ff66964697bc7c944ef989b3fc.
3 Unstandardized coefficient.
Additional information
Notes on contributors
Mengqi Liao
Mengqi Liao (Maggie) (M.A, Pennsylvania State University), Donald P. Bellisario College of Communications at Penn State University. She is interested in investigating the process and psychological effects of human interactions with communication technology, including social media, virtual reality, mobile devices, and emerging artificial intelligence (AI) applications. Her research aims to gain a deep understanding of the effects of different technological affordances on persuasion, users’ cognitive information processing, psychological well-being, and trust.