Mapping E-Cigarette Content on Social Media: A Scoping Review of Methods, AI Applications, and Promotional Strategies

E-cigarette narratives on social media are rapidly evolving alongside the rise of short-form video and advances in artificial intelligence (AI) that enable scalable content analysis. This scoping review mapped research on e-cigarette-related social media content, focusing on methodological approaches (including AI use) and key findings (study domains, promotional strategies, and reporting of additional social media dimensions). Web of Science, Scopus, and PubMed were searched in October 2025 for English-language, peer-reviewed studies published between 2011 and 2025. After independent title/abstract screening by two reviewers and full-text assessment, 136 studies were included. Twitter/X was the most frequently studied platform, followed by Instagram, TikTok, YouTube, and Facebook; only TikTok showed a steady increase in the number of studies. Manual coding predominated, with smaller proportions using computational or hybrid approaches. Computational approaches were generally applied to larger datasets and more consistently reported validation. AI applications were concentrated in topic discovery and sentiment assessment, whereas multimodal approaches such as image classification and sociodemographic inference were uncommon despite the shift toward video-centric platforms. This review advances previous mapping by providing an AI application taxonomy and highlighting methodological gaps in validation practice across manual, computational, and hybrid pipelines. Future studies should expand the use of AI applications alongside transparent and robust validation practices to enable scalable, reliable, and efficient social media surveillance on e-cigarettes.