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Media Studies

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Media Studies

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Research Frontiers in Algorithmic Curation and Content Recommendation Systems

Investigates how machine learning algorithms shape media consumption patterns and audience fragmentation across digital platforms.

Filter Bubbles and Epistemic Closure in Algorithmic Ecosystems
Algorithmic Bias Amplification Across Cultural Contexts
Attention Economy and Cognitive Load in Recommendation Architectures
Opacity and Interpretability in Black-Box Curation Systems
Viral Dynamics and Algorithmic Contagion in Social Networks
Recommender Systems as Ideological Institutions
Personalization Paradox: Diversity Loss Through Preference Learning
Gaming and Manipulation of Algorithmic Recommendation Signals
Algorithmic Gatekeeping and Independent Creator Visibility
Temporal Drift and Algorithmic Stability in Content Distribution

All Media Studies PhD categories