Why’s Everyone on TikTok Now? The Algorithmized Self and the Future of Self-Making on Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it