461 Analysis of the Sunburn Tattoo Trend on Instagram
Bibliographic record
Abstract
From its launch in 2010, the photo-sharing platform Instagram has facilitated the proliferation of global beauty and fashion trends. In recent years, sunburn tattoos have become popular among many users of Instagram. Sunburn tattoos are commonly achieved by covering a small patch of skin with tape or sunscreen while exposing the surrounding skin to the sun with the aim of causing a sunburn. The result is a visible shape, or “tattoo”, in the original skin tone surrounded by a large area of burned skin. The purposes of this study were to determine demographics of participants and the popularity of this trend over time. A search using the hashtag “sunburntattoo” was performed on Instagram. This revealed every post from a user with a public profile who had tagged “sunburntattoo” to their photo. Posts were selected for analysis based on two criteria: the photo must be of a sunburn tattoo and the design of the tattoo must be intentional. The following data were collected on each post: date, number of likes, comments from other accounts, and approximate size (estimated in percent of total body surface area) of the tattoo. Nationality and sex of each user were also recorded. Finally, the user’s type of account was noted (i.e. professional, beauty blog, personal). Of the 121 total comments, only 6 (5%) expressed a negative view of the tattoo. Most posts were made by American users (n=19), followed by German (n=4), Dutch (n=2), Spanish (n=2), Canadian (n=2), and Ecuadorian (n=2) users. The majority of posts (66%) were from personal accounts. Eight posts were made by professional accounts, two of which were dermatology clinics warning against the dangers of sunburn tattoos. Of the posts where the sex of the person with the tattoo was identifiable, 39% were male, and 61% were female. The first photo was posted in 2012, and since then, the greatest number of posts was made in 2015. The average tattoo covered 2% of total body surface area. Recent popularity of sunburn tattoos among Instagram users of different nationality and sex is evident. Posts and comments that explain the risks of this trend exist but are far less common than posts by users sharing photos of their tattoos. Furthermore, the dates of posts suggest that the popularity of this trend peaked in 2015 and has been less prevalent in years since. Providers should be aware of this trend and the role of social media in its proliferation. Prevention efforts may include an increased presence of professional health care accounts on Instagram in order to inform users of the dangers of sunburn tattoos.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".