Modeling Rabbit-Holes on YouTube
Erwan Le Merrer, Gilles Tredan, Ali Yesilkanat

TL;DR
This paper introduces a scalable bot-based method to quantitatively analyze YouTube's rabbit-hole phenomenon, providing a formal theory and large-scale validation to understand how personalized recommendations narrow user feeds.
Contribution
It presents a novel large-scale, automated approach to detect rabbit-holes on YouTube and offers a formal theory explaining their emergence based on user interaction and video attraction.
Findings
Over 16 million recommendations analyzed
Automatic detection of rabbit-holes validated against manual labels
Theoretical model explaining the formation of rabbit-holes
Abstract
Numerous discussions have advocated the presence of a so called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies are often ignoring personalization, are of limited scale, and rely on manual tagging to track this collapse. This precludes a precise understanding of the phenomenon based on reproducible observations, and thus the continuous audits of platforms. In this paper, we first tackle the scale issue by proposing a user-sided bot-centric approach that enables large scale data collection, through autoplay walks on recommendations. We then propose a simple theory that explains the appearance of these RHs. While this theory is a simplifying…
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Taxonomy
TopicsDigital Marketing and Social Media · Opinion Dynamics and Social Influence · Complex Network Analysis Techniques
