Boys and Beauty: Male Makeup Influencers in Tik Tok – A U.S. and China Comparison
Bibliographic record
Abstract
Tik Tok, also known as Dou Yin in its Chinese version, has had a growing reputation in the social networking world since its launching in September 2016. While there are numerous content categories on Tik Tok that attract the many views and likes, beauty and skincare are within the top ten most popular content attracting billions of views. In what used-to-be female influencers dominated field, we witnessed the substantial growth of male makeup influencers with a proportion of them successfully integrated into the worldwide beauty market with high reputation and fame. The objective of this study is to analyze the differences between American and Chinese male makeup influencers as a means to reflect how they differ in aesthetic preference and social factors when creating beauty-related contents. This study employed a mixed-method with both content analyses of the Tik Tok posts as well as a structured scoring system for the appearance of the influencers. The results of the scoring are 7.1 for U.S. influencers and 7.2 for Chinese influencers. Both countries have a very similar feminine appearance with Chinese influencers resulting a slight higher scoring than U.S. influencers. The conclusion of this study rests on the fact that both American and Chinese influencers are emerging in this arena with their own definition of beauty using makeup and other beauty-related products; though they exemplified certain differences in aesthetics preference of appearance, they demonstrated a beauty revolution that is unseen before – through social media which aim to challenge how the society perceive beauty and gender presentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".