| Titre : |
Using computer vision to detect e-cigarette content in TikTok videos |
| Type de document : |
document électronique |
| Auteurs : |
Dhiraj Murthy, Auteur ; Rachel R. Ouellette, Auteur ; Tanvi Anand, Auteur |
| Editeur : |
Oxford University Press |
| Année de publication : |
2024 |
| Collection : |
Nicotine and Tobacco Research num. 26 |
| Importance : |
7 p. |
| Présentation : |
ill., tab. |
| Langues : |
Anglais (eng) |
| Catégories : |
[DIVERS] personne:famille:adolescent
[DIVERS] type de document:vidéo
[TABAC] CANDIDATS:e-cigarette
[TABAC] prévention:campagne:campagne médiatique:internet
|
| Index. décimale : |
TA 1.1.1 Cigarettes (« normales », électroniques, aromatisées,…) |
| Résumé : |
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-cigaretterelated 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 and Methods: 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 (N = 705) for training and 15% (N = 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 videobased 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 imagebased social media platforms to inform and enforce regulations of e-cigarette content on social media. |
| En ligne : |
https://doi.org/10.1093/ntr/ntad184 |
| Format de la ressource électronique : |
Article en ligne |
| Permalink : |
https://biblio.fares.be/opac_css/index.php?lvl=notice_display&id=10241 |
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