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Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

Zidian XieShijian DengPinxin LiuXubin LouChenliang XuDongmei Li
Published in: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco (2024)
Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.
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