Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos

TL;DR

This paper presents an efficient framework for analyzing YouTube's recommendation algorithms for short- and long-form videos, revealing format-specific behavioral patterns and biases in politically sensitive content, with implications for designing fairer AI systems.

Contribution

It introduces a novel data collection method and provides a comparative analysis of biases and behaviors in YouTube's recommendation algorithms across video formats.

Findings

Short-form videos exhibit more immediate engagement but less content diversity.

Recommendation algorithms influence narrative shaping in politically sensitive topics.

Distinct behavioral patterns are observed between short- and long-form video recommendations.

Abstract

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 recommendation algorithms in shaping user experiences. These algorithms significantly influence content consumption, yet concerns about biases, echo chambers, and content diversity persist. This study develops an efficient data collection framework to analyze YouTube's recommendation algorithms for both short-form and long-form videos, employing parallel computing and advanced scraping techniques to overcome limitations of YouTube's API. The analysis uncovers distinct behavioral patterns in recommendation algorithms across the two formats, with short-form videos showing a more immediate shift toward engaging yet less diverse content compared to long-form videos. Furthermore, a novel investigation into biases in politically sensitive topics, such as the South China Sea dispute, highlights the role of these algorithms in shaping narratives and amplifying specific viewpoints. By providing actionable insights for designing equitable and transparent recommendation systems, this research underscores the importance of responsible AI practices in the evolving digital media landscape.