  Filter bubbles have been studied extensively within the context of online
content platforms due to their potential to cause undesirable outcomes such as
user dissatisfaction or polarization. With the rise of short-video platforms,
the filter bubble has been given extra attention because these platforms rely
on an unprecedented use of the recommender system to provide relevant content.
In our work, we investigate the deep filter bubble, which refers to the user
being exposed to narrow content within their broad interests. We accomplish
this using one-year interaction data from a top short-video platform in China,
which includes hierarchical data with three levels of categories for each
video. We formalize our definition of a "deep" filter bubble within this
context, and then explore various correlations within the data: first
understanding the evolution of the deep filter bubble over time, and later
revealing some of the factors that give rise to this phenomenon, such as
specific categories, user demographics, and feedback type. We observe that
while the overall proportion of users in a filter bubble remains largely
constant over time, the depth composition of their filter bubble changes. In
addition, we find that some demographic groups that have a higher likelihood of
seeing narrower content and implicit feedback signals can lead to less bubble
formation. Finally, we propose some ways in which recommender systems can be
designed to reduce the risk of a user getting caught in a bubble.
