Iterative Netnography and Occurrence for Rapid Research: A Case Study of TikTok

As social media becomes more visual, personalized, and algorithmically curated, new methods are needed to understand how content occurs to different communities. Existing API-based and text-centric methods obscure the lived, contextual experience of users — especially in environments like short-form video feeds. This paper presents "iterative netnography," an immersive, team-based methodology developed during a rapid response research project on election rumors in the lead-up to the 2024 U.S. Presidential Election. Building on Kozinets’s netnography, this approach introduces systematic, iterative workflows to study visual algorithmically recommended content in real time and across diverse community archetypes. This paper details the protocol and development of such archetypes, discusses best practices, the types of insights it affords, and contrasts these with API-based methods using a sociotechnical stack framework. It introduces “occurrence:” a sensitizing concept for qualitative social media research, emphasizing the value of studying content as it occurs to users in real-time, dynamic, and high-stakes contexts. Finally, it advocates for collaborative, sustainable, and community-driven cyberinfrastructures to support such work—ensuring that social media research remains resilient amid increasing platform restrictions and sociopolitical pressures.