Artificial Intelligence for effective communication to promote vaping cessation on social media

PROJECT SUMMARY The National Youth Tobacco Survey showed the prevalence of e-cigarette use among high school students has skyrocketed from 12% in 2017 to 28% in 2019 and remains high at 14.1% in 2022. While different regulatory policies (such as the FDA e-cigarette flavor enforcement and Tobacco 21) are currently in place trying to alleviate this vaping epidemic among youth and young adults, there is an urgent need to effectively communicate with the public about the risks of e-cigarette use and nicotine addiction. However, how to engage the public with the e- cigarette prevention messages is particularly challenging. Social media platforms such as Twitter/X, Instagram, TikTok, and YouTube are very popular in the United States, especially among youth and young adults. Previous studies have found that social media platforms are widely used to promote e-cigarette products by vape shops and companies but are under-used by public health authorities for educating the community about the health risks of e-cigarette use. Social media marketing of e-cigarettes as healthier alternatives to conventional cigarettes resulted in the common perception among youth that vaping is a harmless activity. Social media exposure to e-cigarette-related content could affect e-cigarette use (vaping) behavior, with vaping promotion content leading to more vaping and vaping prevention content leading to reduced vaping. Therefore, social media provides a rich and natural data resource about vaping-related messages in different formats (text, image, and video). The purpose of the proposed study is to identify key features of e-cigarette-related social media (Twitter/X, Instagram, TikTok, and YouTube) posts (especially vaping prevention posts) associated with high social media user engagement (such as the number of likes) by applying advanced artificial intelligence and statistical modeling techniques, and further validate them using the combination of an online survey study and a semi-structured interview study. To achieve our goal, we will characterize key features of Twitter/X posts (tweets) related to e-cigarettes associated with high social media user engagement, such as the number of retweets and favorites, using natural language processing, machine learning, and statistical modeling techniques (Aim 1). We will characterize key features of e-cigarette-related Instagram posts associated with high social media user engagement through deep-learning algorithms and statistical models (Aim 2). We will characterize key features of e-cigarette-related TikTok and YouTube videos with high social media user engagement using artificial intelligence techniques (Aim 3). We will validate the key features selected from social media posts through a cross-sectional anonymous online survey study and a semi-structured interview study (Aim 4). Results from the proposed study will provide valuable guidance in designing effective vaping prevention messages for future public health campaigns to help with the effective communication of risks associated with e-cigarette use, which will help prevent the initiation and counter-uptake of e-cigarettes by youth and young adults.