Evidence map›Paper›PMID 39259963›Full record

ArticleJournal of medical Internet research2024

The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study.

Sungwon Jung, Dhiraj Murthy, Bara S Bateineh, Alexandra Loukas, Anna V Wilkinson

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Observational
  6. E-cigarette and Cannabis in Social Media Influencer Marketing and Its Effect on Adolescents: A Survey-Based Experiment.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026
    Article
  7. Article
  8. Article
  9. Article
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  13. Review
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  15. Article
  16. Categorizing E-cigarette-related tweets using BERT topic modeling.Emerging trends in drugs, addictions, and health · 2024
    Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sungwon JungSchool of Journalism and Media, University of Texas at Austin, Austin, TX, United States.ORCID 0009-0002-7401-1194
Dhiraj MurthySchool of Journalism and Media, University of Texas at Austin, Austin, TX, United States.ORCID 0000-0001-9734-1124
Bara S BateinehUniversity of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States.ORCID 0000-0001-9505-3807
Alexandra LoukasDepartment of Kinesiology and Health Education, University of Texas at Austin, Austin, TX, United States.ORCID 0000-0002-5582-9540
Anna V WilkinsonUniversity of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States.ORCID 0000-0002-1805-8863

Funding

Social Media, Acculturation and E-cigarette Use among Mexican American College Students in South TexasR01MD017280 · NIMHD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ALEXANDRA LOUKAS, Anna Victoria Wilkinson · 2022 to 2026
$3.3M
NIMHD NIH HHS R01 MD017280
6 · The paper itself

Abstract

backgroundSocial media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences.

objectiveThis study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtler strategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of social media posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok for youth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements on TikTok.

methodsWe collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, #vapetok and #boxmod). Using the k-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on themes. Next, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machine learning-based model architectures: residual network (ResNet) with 50 layers (ResNet50), Visual Geometry Group model with 16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clustering output. To assess clustering accuracy, we examined 4.01% (441/10,990) of the samples from each video cluster. Finally, we randomly selected 50 videos (5% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation.

resultsWe successfully identified 5 major themes from the TikTok posts. Vape product marketing (1160/10,990, 8.28%) reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer (3775/14,002, 26.96%), general vape (2741/14,002, 19.58%), vape brands (2042/14,002, 14.58%), and vaping cessation (1272/14,002, 9.08%). The ResNet50 model successfully classified clusters based on image features, achieving an average F

conclusionsThe results from both computational and qualitative analyses of text and visual data reveal that vaping is normalized on TikTok. Our identified themes underscore how everyday conversations, promotional content, and the influence of popular figures collectively contribute to depicting vaping as a normal and accepted aspect of daily life on TikTok. Our study provides valuable insights for regulatory policies and public health initiatives aimed at tackling the normalization of vaping on social media platforms.

Indexed as

Natural Language ProcessingSocial MediaVapingAdolescentHumansQualitative Researchcomputer visionelectronic cigarettesnatural language processingsocial mediavaping

Identifiers

PMID39259963
PMCPMC11425021

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.