The pervasive use of short-video applications has raised concerns about their potential negative effects on users, particularly addiction. Existing research often relies on psychological questionnaires, which lack real-world behavioral data, limiting scalability and analytical depth. To address this, we assess the addiction status of short-video platform users using a standardized psychometric questionnaire, combined with platform behavioral data and interview responses to uncover features associated with addiction. Using feature-based modeling, we scale to a dataset of 10,111 addiction-labeled users and identify key indicators of addiction, including prolonged daily watch time, especially at night, and excessive video consumption, while also revealing that higher watch frequency is not fully correlated with addiction. Additionally, we find that addicted users tend to consume a narrower range of content, suggesting a filter bubble effect. Our large-scale analysis provides valuable insights for platform designers, policymakers, and mental health professionals seeking to promote healthier engagement and mitigate the risks of short-video addiction.

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.