Date of Award

6-2-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Information Science

First Advisor

Nitin Agarwal

Abstract

As social media platforms increasingly dominate information consumption, the role of recommendation algorithms in determining user experience has grown both in complexity and impact. This thesis investigates the behavioral patterns and algorithmic preferences embedded within YouTube’s recommendation systems, comparing long- form videos with the rapidly growing category of short-form content, YouTube Shorts. Through a combination of automated data collection, engagement metric analysis, emotional sentiment detection, and toxicity assessment, this study analyzes the evolution of content over successive recommendation depths. Using a controlled digital environment, the research explores how content recommendations change in response to user behavior, including varying watch times for Shorts and metadata separation for long-form videos. The results show that while both formats demonstrate increasing engagement and algorithmic preference for emotionally charged or neutral content, YouTube Shorts exhibit significantly more abrupt shifts in these metrics, mimicking patterns often associated with the addictive designs seen in slot machines. Additionally, evidence indicates that the algorithm prioritizes engagement over potential harms, including the recommendation of increasingly toxic content in some cases. This thesis provides a detailed comparative perspective on YouTube’s long-form and short-form recommendation strategies and raises critical questions about platform accountability, content moderation, and user well-being. The insights presented aim to inform future discussions on ethical algorithm design and the regulation of digital content ecosystems.

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