Short-form video platforms like TikTok play a central role in adolescents’ lives, yet little is known about the actual content they engage with. This study presents a large-scale, computational approach to analyze adolescents’ TikTok feeds using hashtag-based topic modeling applied to user-donated archives from 102 Dutch adolescents (ages 15–19). Using BERTopic, we identified 304 coherent topics, organized them into 32 higher-order content categories, and validated both through a structured human validation study. Analyses reveal that adolescents’ feeds are dominated by entertainment and leisure content, with ‘Media productions,’ ‘Sports,’ ‘Style and appearance,’ ‘Hobbies,’ and ‘Gaming’ as the most prevalent categories. At the same time, there is marked individual variation: niche areas such as K-pop, anime, and specific fandoms are central to some users’ feeds. Latent profile analysis identified six distinct, gendered content-consumption profiles. Together, the findings shed light on adolescents’ TikTok exposure, revealing predominantly benign but highly personalized media diets in which appearance-focused content stands out as a potential risk domain. In all, our approach offers a scalable, efficient way to map short-form video content. Linking these maps to individual users’ media diets opens new possibilities for examining how different content patterns relate to user experiences and outcomes.
Wal et al. (Tue,) studied this question.