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Record W4393389668 · doi:10.1111/srt.13688

Evaluating the effect of social media on the popularity of vampire facial using google trends

2024· letter· en· W4393389668 on OpenAlexaboutno aff
Shazli Razi, Thu M. Truong, Rithi J. Chandy, Babar Rao

Bibliographic record

VenueSkin Research and Technology · 2024
Typeletter
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityVampireSocial mediaAdvertisingComputer sciencePsychologyWorld Wide WebArtificial intelligenceBusinessSocial psychology

Abstract

fetched live from OpenAlex

Vampire facial, also known as a blood facial, uses microneedling with platelet-rich plasma (PRP) to stimulate innate regenerative processes. It was previously used as an injection commonly for orthopedic injuries. In early 2013, Kim Kardashian posted a post-procedure “selfie” on Instagram of her vampire facial.1 The picture she posted, which starkly contrasted the usual photo content of her jet-setting lifestyle, showed Kardashian smizing at the camera with a face covered in bright red blood. The shock factor of the photo caused it to become a viral sensation leading to national and global media coverage.1 Previously, women undergoing aesthetic procedures and treatments went to great lengths to keep their efforts discreet. As a result of Kardashians' photo, women began to imitate her, posting their vampire facial photos on social media. Evolving from a social media trend, her photo normalized the sharing of cosmetic procedures and caused a ripple effect of publicity to a previously unknown procedure. Google trends is a publicly available search analytic tool that can be used to measure internet public interest using keywords.2 We performed a Google Trends analysis to characterize worldwide search trends from October 2010 to October 2020 using the terms vampire facial and blood facial (Chart 1A). Concurrently, a similar spike was observed for the term vampire facial in the category of image searches (Chart 1B). There was a massive rise in the web search hits observed in the month of March 2013 after Kim Kardashian posted her vampire facial selfie. Her post led to a search Interest Over Time (IOT), of 100 representing peak popularity. For reference, search trends for presidential election in the US had an IOT of <25 until the election years 2016 (IOT 32) and 2020 (IOT 100) in the U.S. Vermont, Wyoming, and North Dakota were the states with the highest search hits for the term vampire facial during the spike in March 2023. At the same time, Maryland, South Carolina, and Oklahoma were the states with the highest search hits for the keyword blood facial (Figure 1). This social media trend also spread across the globe as the term vampire facial had increased searches in the United Kingdom, Ireland, and Australia compared to the U.S. After America, the term blood facial was most searched in South Africa, Australia, and Nigeria. According to Google trends, the worldwide average IOT was between 0 and 1 for vampire facial before 2013, but from 2013−2020 the monthly average IOT was 11.4. An analysis of the top three English language newspapers in the USA, Canada, Ireland, UK, Australia, and New Zealand by Rachul et al. found a spike in articles related to platelet rich plasma or PRP (both medical and cosmetic uses) published from 2013−2014.3 In the U.S., among all newspapers, The New York Times published the most articles (66) on PRP from 2009−2015, followed by the Wall Street Journal and USA Today. Rachul et al. found that most of these articles portrayed PRP as a routine procedure, despite its obscurity years prior to Kardashian's post. Today, PRP with microneedling is used for skin rejuvenation, acne scars, melasma, androgenic alopecia, striae, axillary hyperhidrosis and various other indications.4 In conclusion, medical procedures sensationalized by celebrities increase consumer demand and drive the development, research, and refinement of medical aesthetic procedures.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.204
GPT teacher head0.494
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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