| Titre : |
Scalable surveillance of e-cigarette products on Instagram and TikTok using computer vision |
| Type de document : |
document électronique |
| Auteurs : |
Julia Vassey, Auteur ; Chris J. Kennedy, Auteur ; Chang Ho-Chun Herbert, Auteur |
| Editeur : |
Oxford University Press |
| Année de publication : |
2024 |
| Collection : |
Nicotine and Tobacco Research |
| Importance : |
24 p. |
| Langues : |
Anglais (eng) |
| Catégories : |
[TABAC] CANDIDATS:e-cigarette
[TABAC] économie du tabac:marketing:publicité:publicité pro-tabac:publicité directe
[TABAC] prévention:campagne:campagne médiatique:internet
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| Mots-clés : |
réseaux sociaux, informatique |
| Index. décimale : |
TA 1.1.1 Cigarettes (« normales », électroniques, aromatisées,…) |
| Résumé : |
Introduction:
Instagram and TikTok, video-based social media platforms popular among adolescents, contain tobacco-related content despite the platforms’ policies prohibiting substance-related posts. Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos.
Methods:
We created a dataset of 6,999 Instagram images labeled for 8 object classes: mod or pod devices, e-juice containers, packaging boxes, nicotine warning labels, e-juice flavors, e-cigarette brand names, and smoke clouds. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model’s performance on 20 Instagram and TikTok videos, and applied the model to 14,072 e-cigarette-related promotional TikTok videos (2019-2022; 10,276,485 frames).
Results:
The model achieved the following mean average precision scores on the image test set: e-juice container: 0.89; pod device: 0.67; mod device: 0.54; packaging box: 0.84; nicotine warning label: 0.86; e-cigarette brand name: 0.71; e-juice flavor name: 0.89; and smoke cloud: 0.46. The largest number of TikTok videos – 9,091 (65%) - contained smoke clouds, followed by mod and pod devices detected in 6,667 (47%) and 5,949 (42%) videos respectively. Prevalence of nicotine warning labels was the lowest, detected in 980 videos (7%).
Conclusions:
Deep learning-based object detection technology enables automated analysis of visual posts on social media. Our computer vision model can detect the presence of e-cigarettes products in images and videos, providing valuable surveillance data for tobacco regulatory scienc.
Implications :
● Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos.
● We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model’s performance on 20 Instagram and TikTok videos featuring at least two e-cigarette objects, and applied the model to 14,072 e-cigarette-related promotional TikTok videos (2019-2022; 10,276,485 frames).
● The deep learning model can be used for automated, scalable surveillance of image- and video-based e-cigarette-related promotional content on social media, providing valuable data for tobacco regulatory science. Social media platforms could use computer vision to identify tobacco-related imagery and remove it promptly, which could reduce adolescents’ exposure to tobacco content online. |
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