Project Summary Use of vaping products (e.g., electronic nicotine delivery systems, e-cigarettes) has been increasing rapidly, particularly among teens and young adults. With limited information on the long-term effects of vaping products, health information about vaping has been somewhat unclear in regards to associated health risks. Teens and young adults may be reluctant to disclose their use of vaping products to parents or health providers and instead turn to social media to share and seek out information regarding vaping risks and cessation supports. Given the ubiquitous use of social media platforms among this population and the ability for advanced artificial intelligence (AI) and natural language processing (NLP) technologies to analyze content shared on social media platforms, there is strong potential for this information to be leveraged and used to detect and reach out to those most at risk for negative health outcomes caused by vaping. Thus, our current proposal outlines the use of detection models to identify teens and young adults socially networking about vaping, the use of a chatbot to screen for the needs of eligible users, and the use of a digital intervention system (i.e., quitSTART with an embodied chatbot) aimed to support vaping cessation efforts by increasing risk awareness and decreasing pro-vaping attitudes. In Aim #1a, we will develop an intelligent detection system by leveraging state-of-the-art machine learning, deep learning, and NLP techniques for mining massive social media data on vaping with clinical inputs. This detection system will implement multiple functionalities on both Twitter and Reddit social media platforms to identify posts regarding the use of vaping products, negative health outcomes experienced, and interest in vaping cessation. To evaluate the validity and specificity of the detection model developed on both platforms, we will also conduct surveys among a subsample of those identified (N=100) to rule out false positives and to gather data on vaping behaviors, social media content generation about vaping, motivations for vaping product use, and interest in vaping cessation to refine the developed models in Aim #1b. In Aim #2, we will develop both a chatbot to screen individuals identified on social media as well as an in-app chatbot to guide users to tailored content, conduct daily assessments and check-ins, motivate and encourage their cessation efforts, and promote sustained user engagement within a widely-used evidence-based mobile application (app) intervention for combustible smoking, quitSTART. We will conduct usability and acceptability testing on both levels of the chatbot among a sample of participants (N=30) recruited in Aim #1b. In Aim #3, we aim to integrate the developed detection model, chatbot screener, and adapted mobile app into a streamlined outreach and intervention system, and conduct a randomized controlled trial (N= 189) to evaluate user engagement with and preliminary efficac...