  The role of recommendation algorithms in online user confinement is at the
heart of a fast-growing literature. Recent empirical studies generally suggest
that filter bubbles may principally be observed in the case of explicit
recommendation (based on user-declared preferences) rather than implicit
recommendation (based on user activity). We focus on YouTube which has become a
major online content provider but where confinement has until now been
little-studied in a systematic manner. Starting from a diverse number of seed
videos, we first describe the properties of the sets of suggested videos in
order to design a sound exploration protocol able to capture latent
recommendation graphs recursively induced by these suggestions. These graphs
form the background of potential user navigations along non-personalized
recommendations. From there, be it in topological, topical or temporal terms,
we show that the landscape of what we call mean-field YouTube recommendations
is often prone to confinement dynamics. Moreover, the most confined
recommendation graphs i.e., potential bubbles, seem to be organized around sets
of videos that garner the highest audience and thus plausibly viewing time.
