5 papers
A Keyframe-Based Approach for Auditing Bias in YouTube Shorts Recommendations
Mert Can Cakmak, Nitin Agarwal
YouTube Shorts and other short-form video platforms now influence how billions engage with content, yet their recommendation systems remain largely opaque. Small shifts in promoted…
TriPSS: A Tri-Modal Keyframe Extraction Framework Using Perceptual, Structural, and Semantic Representations
Mert Can Cakmak, Nitin Agarwal, Diwash Poudel
Efficient keyframe extraction is critical for video summarization and retrieval, yet capturing the full semantic and visual richness of video content remains challenging. We introd…
Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos
Selimhan Dagtas, Mert Can Cakmak, Nitin Agarwal
The growing popularity of short-form video content, such as YouTube Shorts, has transformed user engagement on digital platforms, raising critical questions about the role of recom…
Investigating Algorithmic Bias in YouTube Shorts
Mert Can Cakmak, Nitin Agarwal, Diwash Poudel
The rapid growth of YouTube Shorts, now serving over 2 billion monthly users, reflects a global shift toward short-form video as a dominant mode of online content consumption. This…
Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations
Selimhan Dagtas, Mert Can Cakmak, Nitin Agarwal
Short-form video platforms such as YouTube Shorts increasingly shape how information is consumed, yet the effects of engagement-driven algorithms on content exposure remain poorly…