Not Just 'For You': How the Algorithmic Crystal Mediates Communication and Identity Work on TikTok's FYP

Personalized algorithms are central to how people discover information and engage with media online. Drawing on interviews and screen-sharing sessions with TikTok users (N=27), we extend the algorithmic crystal framework, which conceptualizes personalized algorithms as reflective surfaces through which users may interpret their experiences with content in relation to their own self-concepts. This research expands the framework to account for the interpersonal dynamics that emerge from user engagement with algorithmic feeds. We found that users who feel ''seen'' by the algorithm use its personalized content recommendations for social signaling: sharing content that represents themselves (''this is me''), acknowledges how they see others (''this is you''), and affirms shared identities (''this is us''). We suggest that these dynamics give rise to a hybrid form of digital selfhood simultaneously shaped by algorithmic profiling and networked social interaction-blurring existing separations in digital identity theory. We also build on the concept of diffracted belonging-the experience of recognizing aspects of oneself in the content of diverse others-to explore how users interpret algorithmically-recommended content as reflective of the self. Our findings suggest that such moments of recognition may contribute to shifts in self-perception and support ongoing processes of identity development. Finally, we illustrate how users engage in the strategic refinement of their feeds to manage how they feel while using the platform. Our findings suggest that this process involves reflective, and sometimes effortful, negotiation with the algorithm, highlighting the co-produced nature of mood management in everyday human-algorithm interactions. Together, these findings underscore the interpersonal and psychological dynamics of interacting with personalized algorithms and provide insights into how social communication and identity work unfold in algorithmically-mediated environments.