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

Vista sencilla de ítem
dc.creatorSiles González, Ignacio
dc.creatorMeléndez Moran, Ariana
dc.date.accessioned2021-04-14T18:48:17Z
dc.date.available2021-04-14T18:48:17Z
dc.date.issued2021-05-27
dc.description.abstractThis 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.es_ES
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Centro de Investigación en Comunicación (CICOM)es_ES
dc.identifier.urihttps://hdl.handle.net/10669/83230
dc.language.isoenges_ES
dc.rightsacceso abierto
dc.sourceInternational Communication Association (ICA) Mayo 27-31, 2021es_ES
dc.subjectadictiones_ES
dc.subjectsocial media algorithmes_ES
dc.title“The most aggressive of algorithms”: User awareness of and attachment to TikTok’s content personalizationes_ES
dc.typecomunicación de congreso

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
TikTok_ICA_Main_2_Cover.pdf
Tamaño:
188.68 KB
Formato:
Adobe Portable Document Format
Descripción:
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
2.83 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Comunicación colectiva