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This study investigates the sociotechnical dynamics of recommendation-driven collectives by examining "RushTok" as an archetype of an event-based algorithmic community assembled around an offline ritual. The authors find a stark decoupling between perceived community and actual affiliation—72% viewed it as a community while only 15% claimed membership—driven by parasocial attention to key creators alongside defensive creator boundary-work against external engagement. The work underscores the need for algorithmic governance frameworks to manage temporal, high-intensity recommendation spikes through mechanisms like graduated visibility and post-viral aftercare.
Recommendation algorithms can manufacture massive, hyper-visible subcultures around real-world events overnight, yet the users driving them largely reject the identity of being community members.
Each August, TikTok's For You page turns the University of Alabama's sorority recruitment into RushTok. We examine RushTok as an event-based algorithmic community: a collective assembled around a bounded offline ritual and sustained by recommendation. Using a mixed-methods survey (n=71) and a reflexive account of creator outreach, we ask who participates, how, and with what stakes. Findings show an ambiguous and entertainment based throughline; many called it a community (51/71) but few claimed membership (11/71). Affiliation centered on creators rather than shared practices, with parasocial attention clustering around a small set of potential new members (PNMs) and returning figures. Higher content exposure tracked with self-identification as a community member; those members commented, followed creators, and engaged across videos. Attempts to interview creators were met with silence or refusals, reflecting community boundary-work despite viral visibility. We outline implications for platform governance, including time-bounded context, graduated visibility, and aftercare.