“The most aggressive of algorithms”: User awareness of and attachment to TikTok’s content personalization

 

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Autoři: Siles González, Ignacio, Meléndez Moran, Ariana
Médium: comunicación de congreso
Datum vydání:2021
Popis:This paper examines how a group of TikTok users in Costa Rica made sense of the workings of its algorithmic content personalization, how they came to this understanding, and what the implications of their self-proclaimed awareness are for establishing a particular affective relationship with the app. Drawing on actor-network theory, we argue that the awareness that these users have of algorithms shapes their affective attachment to TikTok (which they often describe as a form of “addiction”). The paper examines how users carefully enacted various practical roles in order to maintain the affect associated with personalized content on the app. In this way, we add nuance to dominant accounts of the user-algorithm relationship. Rather than viewing it as constant, fixed, and universal, we argue for considering it as “always in the making.” The paper shows how this relationship undergoes multiple “passages” through which distinct capacities for both users and algorithms emerge.
Země:Kérwá
Instituce:Universidad de Costa Rica
Repositorio:Kérwá
Jazyk:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/83230
On-line přístup:https://hdl.handle.net/10669/83230
Klíčové slovo:adiction
social media algorithm