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The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study.
Jung, Sungwon; Murthy, Dhiraj; Bateineh, Bara S; Loukas, Alexandra; Wilkinson, Anna V.
Afiliación
  • Jung S; School of Journalism and Media, University of Texas at Austin, Austin, TX, United States.
  • Murthy D; School of Journalism and Media, University of Texas at Austin, Austin, TX, United States.
  • Bateineh BS; University of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States.
  • Loukas A; Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX, United States.
  • Wilkinson AV; University of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States.
J Med Internet Res ; 26: e55591, 2024 Sep 11.
Article en En | MEDLINE | ID: mdl-39259963
ABSTRACT

BACKGROUND:

Social 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.

OBJECTIVE:

This 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.

METHODS:

We 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.

RESULTS:

We 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 F1-score of 0.97, the highest among the 3 models. Qualitative content analyses indicated that vaping was depicted as a normal, routine part of daily life, with TikTok influencers subtly incorporating vaping into popular culture (eg, gaming, skateboarding, and tattooing) and social practices (eg, shopping sprees, driving, and grocery shopping).

CONCLUSIONS:

The 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Medios de Comunicación Sociales / Vapeo Límite: Adolescent / Humans Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / Medios de Comunicación Sociales / Vapeo Límite: Adolescent / Humans Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá