Simulating User Watch-Time to Investigate Bias in YouTube Shorts Recommendations

TL;DR

This paper uses simulation of user watch behaviors on YouTube Shorts to analyze how engagement-driven algorithms affect content diversity, relevance, and bias, revealing patterns of topic drift and amplification.

Contribution

It introduces a simulation framework to study how different viewing behaviors impact recommendation relevance and bias in short-form video platforms.

Findings

Engagement behaviors influence content relevance and topical drift.

Simulation reveals amplification and topic generalization patterns.

Results have implications for content diversity and platform accountability.

Abstract

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 understood. This study investigates how different viewing behaviors, including fast scrolling or skipping, influence the relevance and topical continuity of recommended videos. Using a dataset of over 404,000 videos, we simulate viewer interactions across both broader geopolitical themes and more narrowly focused conflicts, including topics related to Russia, China, the Russia-Ukraine War, and the South China Sea dispute. We assess how relevance shifts across recommendation chains under varying watch-time conditions, using GPT-4o to evaluate semantic alignment between videos. Our analysis reveals patterns of amplification, drift, and topic generalization, with significant implications for content diversity and platform accountability. By bridging perspectives from computer science, media studies, and political communication, this work contributes a multidisciplinary understanding of how engagement cues influence algorithmic pathways in short-form content ecosystems.