Examining TikTok’s Sport Sponsorship Strategy: Content Creation and Amplification in the Global North
Bibliographic record
Abstract
This study investigates social media platform TikTok’s global sport sponsorship strategy, seeking to explore the geopolitical motivations and implications of the firm’s partnerships over a four-year period. Taking a multifaceted approach, a network analysis of the brand’s sponsorship agreements was undertaken over a four-year period from 2020 to 2023, providing insight into the platform’s use of commercial partnerships to access and integrate within the Global North. Central to this analysis is the recognition of TikTok’s place and prominence within the growing attention economy of social and digital media, and the brand’s efforts to legitimize and stabilize its place within Western markets where its legal standing has been challenged. The study’s findings highlight the growing platformization of political sponsorship strategy, evolving beyond merely the exertion of soft power and increasingly emphasizing the integration and amplification of brand presence in core markets. Here, this work offers a new lens through which to examine sponsorship networks and relations and highlight the increasingly prevalent political dimension of sponsorship strategy on an international level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".