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Why’s Everyone on TikTok Now? The Algorithmized Self and the Future of Self-Making on Social Media
Open accessAbstract
The video-sharing social media platform TikTok has experienced a rapid rise in use since its release in 2016. While its popularity is undeniable, at the first glance, it seems to offer features already available on previously existing and well-established platforms such as Instagram, YouTube, and Facebook. To understand processes of self-making on TikTok, we undertake two methods of data collection: a walkthrough of the app and its surrounding environment, and 14 semistructured participant interviews. A qualitative analysis of this data finds three distinct themes emerge: (1) awareness of the algorithm, (2) content without context, and (3) self-creation across platforms. These results show that TikTok departs from existing platforms in the model of self-making it engenders, which we term “the algorithmized self”—a complication of the pre-existing “networked self” framework.
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“Consequently, TikTok’s algorithmic prominence results in the creation of a personalized “algorithmic version” of each user, categorized according to predefined schemas [93–95].”
From Scrolling through adolescence: a systematic review of the impact of TikTok on adolescent mental health (2025) · cites this paper as [93]
In the citing paper
“Bhandari and Bimo [19] examine TikTok's recommendation algorithm and its effects on the 'algorithmized self', revealing recursive dynamics whereby individuals modify behavior to satisfy algorithmic preferences.”
From Encountering Generative AI: Narrative Self-Formation and Technologies of the Self Among Young Adults (2026) · cites this paper as [19]
In the citing paper
“Where existing research examines how algorithms shape identity as social positioning and role performance [10,19], this analysis foregrounds how AI systems participate in constituting selfhood itself: organizing subjective experience through narrative coherence, interpretive authority, and self-governance practices.”
From Encountering Generative AI: Narrative Self-Formation and Technologies of the Self Among Young Adults (2026) · cites this paper as [19]
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Metadata
- Authors
- Bhandari, A., & Bimo, S.
- Affiliations
- Cornell University (US)
- York University (CA)
- Corresponding author
- Bhandari, Aparajita
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 8
- Issue
- 1
- ISSN
- 2056-3051 (ISSN-L)
- OpenAlex
- W4221035882
- Cited by
- 491
- References
- 20
- Citations by year
- 2026: 143 · 2025: 155 · 2024: 109 · 2023: 71 · 2022: 13
- FWCI
- 38.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Social Media + Society (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Communication › Media and Digital Communication
- Social Sciences › Gender Studies › Gender, Feminism, and Media
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- TikTok, self-creation, social networking sites, algorithm awareness, networked self, online video platforms, self-presentation, platform comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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