TIKTOK AND THE “ALGORITHMIZED SELF”: A NEW MODEL OF ONLINEINTERACTION
Bibliographic record
Abstract
Since its release in 2017, the video sharing app TikTok has been downloaded 1.5 billion times. While its popularity has been attributed to the abundance of celebrity users, its interactive features, and its short, palatable video length, it has been the subject of relatively few academic studies. This project employs the walkthrough method to examine TikTok within the context of identity negotiation and self-representation on social media. More specifically, it seeks to understand whether TikTok follows a precedent set by other Social Networking Sites which support users self-representing via sociability “to the network, via the network”; i.e. by interacting within the affordances of the platform, which may include sharing, liking, commenting, etc (Papacharissi, 2013). This model ostensibly offers users a stage where they may display their individuality and curate content that reflects their personal interests. By regularly using the app for a period of a month and collecting extensive field notes, screenshots, and video recordings, we found that TikTok’s version of sociality differs from that offered by other SNSs. While other sites purport to be a tool with which users may represent their identities, TikTok does away with this conceit by engendering a mode of sociality (through its design features and affordances) in which the crux of interaction is not between users and their social network, but between a user and what we call an “algorithmized” version of self. This finding has the potential to enrich and complicate the discourse surrounding online identity formation and sociality.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".