TIKTOK AND THE “ALGORITHMIZED SELF”: A NEW MODEL OF ONLINE INTERACTION

  • Aparajita Bhandari Cornell University
  • Sara Bimo York University
Keywords: walk-through method, tiktok, algorithms, identity, representation

Abstract

Since its release in 2017, the video sharing app TikTok has been downloaded 1.5 billion times. While its popularity has been attributed to the abundance of celebrity users, its interactive features, and its short, palatable video length, it has been the subject of relatively few academic studies. This project employs the walkthrough method to examine TikTok within the context of identity negotiation and self-representation on social media. More specifically, it seeks to understand whether TikTok follows a precedent set by other Social Networking Sites which support users self-representing via sociability “to the network, via the network”; i.e. by interacting within the affordances of the platform, which may include sharing, liking, commenting, etc (Papacharissi, 2013). This model ostensibly offers users a stage where they may display their individuality and curate content that reflects their personal interests. By regularly using the app for a period of a month and collecting extensive field notes, screenshots, and video recordings, we found that TikTok’s version of sociality differs from that offered by other SNSs. While other sites purport to be a tool with which users may represent their identities, TikTok does away with this conceit by engendering a mode of sociality (through its design features and affordances) in which the crux of interaction is not between users and their social network, but between a user and what we call an “algorithmized” version of self. This finding has the potential to enrich and complicate the discourse surrounding online identity formation and sociality.

Published
2020-10-05
How to Cite
Bhandari, A., & Bimo, S. (2020). TIKTOK AND THE “ALGORITHMIZED SELF”: A NEW MODEL OF ONLINE INTERACTION. AoIR Selected Papers of Internet Research, 2020. https://doi.org/10.5210/spir.v2020i0.11172
Section
Papers B