Using Computer Vision to Detect E-cigarette Content in TikTok Videos.
Nicotine Tob Res
; 26(Supplement_1): S36-S42, 2024 Feb 15.
Article
em En
| MEDLINE
| ID: mdl-38366342
ABSTRACT
INTRODUCTION:
Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used social media platforms among youth, such as TikTok, contain primarily visual content, requiring the ability to detect e-cigarette-related content across large sets of videos and images. This study aims to use a computer vision technique to detect e-cigarette-related objects in TikTok videos. AIMS ANDMETHODS:
We searched 13 hashtags related to vaping on TikTok (eg, #vape) in November 2022 and obtained 826 still images extracted from a random selection of 254 posts. We annotated images for the presence of vaping devices, hands, and/or vapor clouds. We developed a YOLOv7-based computer vision model to detect these objects using 85% of extracted images (Nâ =â 705) for training and 15% (Nâ =â 121) for testing.RESULTS:
Our model's recall value was 0.77 for all three classes vape devices, hands, and vapor. Our model correctly classified vape devices 92.9% of the time, with an average F1 score of 0.81.CONCLUSIONS:
The findings highlight the importance of having accurate and efficient methods to identify e-cigarette content on popular video-based social media platforms like TikTok. Our findings indicate that automated computer vision methods can successfully detect a range of e-cigarette-related content, including devices and vapor clouds, across images from TikTok posts. These approaches can be used to guide research and regulatory efforts. IMPLICATIONS Object detection, a computer vision machine learning model, can accurately and efficiently identify e-cigarette content on a primarily visual-based social media platform by identifying the presence of vaping devices and evidence of e-cigarette use (eg, hands and vapor clouds). The methods used in this study can inform computational surveillance systems for detecting e-cigarette content on video- and image-based social media platforms to inform and enforce regulations of e-cigarette content on social media.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mídias Sociais
/
Sistemas Eletrônicos de Liberação de Nicotina
Limite:
Adolescent
/
Humans
Idioma:
En
Revista:
Nicotine Tob Res
Assunto da revista:
SAUDE PUBLICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos