How to train your algorithm: The struggle for public control over private audience commodities on Tiktok

Abstract

Social media users are increasingly aware of the politics of their viewing habits, and they attempt to express these politics through interactions with proprietary algorithms. Combining theories about audience commodities with scholarship about “algorithmic imaginaries,” I define “algorithmically imagined audiences” as a kind of algorithmic imaginary, and I analyze 103 TikTok videos to explore how people attempt to politically engage with algorithms to position themselves within audiences. Although algorithms and audiences are proprietary, TikTokers believe they can reassert public control over audience commodities to engage in counterpublic world-making and to re-position themselves within imagined communities. While these practices are impactful, they have conceptual and practical limits; these same tactics are used to reprivatize audience commodities and to reinscribe the neoliberal capitalist underpinnings. This article raises questions for future researchers about the opportunities and limits of sociotechnical beliefs.

Assembling “Sides” of TikTok: Examining Community, Culture, and Interface through a BookTok Case Study

“…(p. 1441) By creating a new account and seeking out BookTok content through search, we enacted such strategies to avoid these feedback loops. This is related to reason two, which even scholarship on the TikTok algorithm acknowledges is not the only way users find content (Cotter et al, 2022;Jones, 2023;Schellewald, 2021). By only seeking out content via the For You Page, scholars ignore users who want to find communities on TikTok after they have signed up.…”

“…The internet has been thought of as a third place, distinct from home and work (Baym, 2015), and spatial metaphors help further distinguish this. On TikTok, Corinne Jones (2023) found "people often conceive of TikTok in spatial terms . .…”

“…The feeling users experience within these algorithmically driven imagined communities has been referred to as "algorithmic closeness," in which users feel bonded together due to content pushed to them via the algorithm (Krutrök, 2021), as well as the "for you feeling" (Schellewald, 2021), in which individuals see themselves reflected back via the algorithm. These are "algorithmically imagined audiences" (Jones, 2023) that view themselves as relational to fellow community members through the algorithm in addition to texts. It is almost important to recognize users are not the only ones who imagine audiences and communities.…”

“…While much scholarly attention has been paid to the TikTok algorithm as community and identity formation (Cotter et al, 2022; Jones, 2023), there are other features at work. It is also important to note how the algorithm invokes feelings of closeness or relationality, and Jones’s (2023) work helps to suture technologies more clearly to texts, imagined communities, and audiences.…”

“…While much scholarly attention has been paid to the TikTok algorithm as community and identity formation (Cotter et al, 2022; Jones, 2023), there are other features at work. It is also important to note how the algorithm invokes feelings of closeness or relationality, and Jones’s (2023) work helps to suture technologies more clearly to texts, imagined communities, and audiences. The feeling users experience within these algorithmically driven imagined communities has been referred to as “algorithmic closeness,” in which users feel bonded together due to content pushed to them via the algorithm (Krutrök, 2021), as well as the “for you feeling” (Schellewald, 2021), in which individuals see themselves reflected back via the algorithm.…”