Cited by 132Innovative Human-Technology InteractionPrivacy, Security, and Data ProtectionMedia and Digital Communication

The Algorithmic Crystal: Conceptualizing the Self through Algorithmic Personalization on TikTok

Lee, Angela Y., Mieczkowski, Hannah, Ellison, Nicole B., Hancock, Jeffrey T.·2022·Proceedings of the ACM on Human-Computer Interaction

Abstract

This research examines how TikTok users conceptualize and engage with personalized algorithms on the TikTok platform. Using qualitative methods, we analyzed 24 interviews with TikTok users to explore how algorithmic personalization processes inform people's understanding of their identities as well as shape their orientation to others. Building on insights from our qualitative data and previous scholarship on algorithms and identity, we propose a novel conceptual model to understand how people think about and interact with personalized algorithmic systems. Drawing on the metaphor of crystals and their properties, the algorithmic crystal framework is an analytic frame that captures user understandings of how personalized algorithms (1) interact with user identity by reflecting user self-concepts that are both multifaceted and dynamic and (2) shape perspectives on others encountered through the algorithm, by orienting users to recognize parts of themselves refracted in other users and to experience ephemeral, diffracted connections with groups of similar others. We describe how the algorithmic crystal framework can extend theory and inform new lines of research around the implications of algorithms in self-concept development and social life.

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Crossref · 10.1145/3555601

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59 of 59 references verified0 citation errors · 0 not checkable

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  • TikTok's algorithmic prominence results in the creation of a personalized "algorithmic version" of each user, categorized according to predefined schemas.

    “Consequently, TikTok’s algorithmic prominence results in the creation of a personalized “algorithmic version” of each user, categorized according to predefined schemas [93–95].”

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Authors
Lee, A. Y., Mieczkowski, H., Ellison, N. B., & Hancock, J. T.
Affiliations
  • Stanford University (US)
  • University of Michigan (US)
Venue
Proceedings of the ACM on Human-Computer Interaction
Published
2022-11-07
Volume
6
Issue
CSCW2
Pages
1-22
ISSN
2573-0142 (ISSN-L)
OpenAlex
W4309617554
Cited by
132
References
59
Citations by year
2026: 33 · 2025: 45 · 2024: 36 · 2023: 17 · 2022: 1
FWCI
12.24
Type
journal article
Language
en
Open access
closed
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
Keywords
algorithmic personalization, TikTok, self-concept, identity, social media algorithms, self-reflection, social connectedness, conceptual model

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