The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study (Preprint)
Authors: Jung Sungwon , Murthy Dhiraj , Bateineh Bara S , Loukas Alexandra , Wilkinson Anna V 
Published: December 2023

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, <i>#vapetok</i> and <i>#boxmod</i>). 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. <i>Vape product marketing</i> (1160/10,990, 8.28%) reflected direct marketing, while the other 4 themes reflected indirect marketing: <i>TikTok influencer</i> (3775/14,002, 26.96%), <i>general vape</i> (2741/14,002, 19.58%), <i>vape brands</i> (2042/14,002, 14.58%), and <i>vaping cessation</i> (1272/14,002, 9.08%). The ResNet50 model successfully classified clusters based on image features, achieving an average <i>F</i><sub>1</sub>-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.