  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 viewpoint on a complex and
planet-wide phenomenon, it carries multiple advantages: it can be analytically
modeled, and provides a general yet rigorous definition of RHs. We define them
as an interplay between i) user interaction with personalization and ii) the
attraction strength of certain video categories, which cause users to quickly
step apart of mainstream recommendations made to fresh user profiles. We
illustrate these concepts by highlighting some RHs found after collecting more
than 16 million personalized recommendations on YouTube. A final validation
step compares our automatically-identified RHs against manually-identified RHs
from a previous research work. Together, those results pave the way for large
scale and automated audits of the RH effect in recommendation systems.
