Vassey Julia, Kennedy Chris J, Chang Ho-Chun Herbert, Unger Jennifer B
Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA.
Tob Control. 2025 Feb 6. doi: 10.1136/tc-2024-058887.
Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal of their promotional content, for example, depicting e-cigarettes alongside healthy lifestyle or entertainment imagery that could decrease youths' risk perceptions of e-cigarettes. Monitoring the prevalence of such content on social media using computer vision and generative AI (artificial intelligence) can provide valuable data for tobacco regulatory science (TRS).
We selected 102 Instagram and TikTok videos posted by micro-influencers in 2021-2024 who promoted e-cigarettes alongside posts featuring four themes: cannabis, entertainment, fashion or healthy lifestyle. We used OpenAI's GPT-4o multimodal large-scale visual linguistic model to detect the presence of nicotine vaping, cannabis vaping, fashion, entertainment and healthy lifestyle. The model did not require any additional training and improved its performance as we modified the text prompt.
The model's accuracy was 87% for nicotine vaping, 96% for cannabis vaping, 99% for fashion, 96% for entertainment and 98% for healthy lifestyle.
Generative AI can achieve accurate object detection with zero-shot learning (no additional training of the pretrained model). This model can be applied to big data-scale sample sizes of images and videos to detect e-cigarette-related and other substance-related promotional content and contexts (eg, healthy lifestyle) used for the promotion of these products on social media, providing valuable data for TRS.
在Instagram或TikTok上为烟草品牌推广电子烟的社交媒体有影响力者会使用营销策略来提高其促销内容的吸引力，例如，将电子烟与健康生活方式或娱乐形象一起呈现，这可能会降低青少年对电子烟的风险认知。利用计算机视觉和生成式人工智能（AI）监测此类内容在社交媒体上的流行情况可为烟草监管科学（TRS）提供有价值的数据。
我们选取了2021年至2024年由微有影响力者发布的102条Instagram和TikTok视频，这些视频在推广电子烟的同时还带有四个主题的帖子：大麻、娱乐、时尚或健康生活方式。我们使用OpenAI的GPT-4o多模态大规模视觉语言模型来检测尼古丁 vaping、大麻vaping、时尚、娱乐和健康生活方式的存在。该模型无需任何额外训练，并且随着我们修改文本提示，其性能得到了提升。
该模型对尼古丁vaping的准确率为87%，对大麻vaping的准确率为96%，对时尚的准确率为99%，对娱乐的准确率为96%，对健康生活方式的准确率为98%。
生成式人工智能可以通过零样本学习（无需对预训练模型进行额外训练）实现准确的目标检测。该模型可应用于图像和视频的大数据规模样本，以检测与电子烟相关以及用于在社交媒体上推广这些产品的其他物质相关的促销内容和背景（如健康生活方式），为烟草监管科学提供有价值的数据。
