THE POLITICS AND EVOLUTION OF TIKTOK AS PLATFORM TOOL
Bibliographic record
Abstract
A fast-growing international success, ByteDance’s short video platform TikTok is a relevant case study to examine how digital platforms expand infrastructurally and accumulate power. TikTok has achieved popularity comparable to major players, including Facebook, Instagram, and Snapchat. It now grapples with balancing the diverse interests of its different user groups, chief among which content creators. We interrogate how TikTok manages this challenge via an exploratory study that studies the platform’s evolution through what we dub ‘platform tools,’ or, the software-based instruments for cultural production on social media platforms. Such software-based tools have been previously theorized using the ‘boundary resources’ framework, which emerged from information systems studies. This framework conceptualizes platform tools as interrelated, contextual, and dynamic, changing in response to variables internal and external to the platform ecosystem. Recognizing that platform tools are ever-changing, we conduct a ‘platform historiography’ to periodize three main trends: platform tools (1) have contributed to the formalization and professionalization of platform content; (2) have encouraged the standardization of platform-dependent cultural production; and (3) have furthered the platformization of TikTok both within, as well as outside the cultural industries. Our paper serves as a response to calls from media scholars to view platforms as contingent and ever-evolving, and to further social media historiography. More specifically, we contribute to the literature on platform studies because it focuses on an understudied aspect of platform governance: platform tools.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".