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.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".