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Nudging recommendation algorithms increases news consumption and diversity on YouTube
Xudong Yu1, Muhammad Haroon2, Ericka Menchen-Trevino3
1Department of Communication, University of North Dakota, Grand Forks, USA.
PNAS Nexus
|December 2, 2024
Summary
This study mitigated social media algorithm biases by nudging YouTube’s recommendation system with balanced news content. This intervention increased news consumption and reduced ideological bias without harming user engagement.
Area of Science:
- Social Media Algorithms
- Computational Social Science
- Information Science
Background:
- Recommendation algorithms on social media platforms significantly influence user attention and information consumption patterns.
- Key biases include interest bias (narrowing content to non-news/entertainment) and ideological bias (reinforcing partisan viewpoints).
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