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Background: On October 11, 2023 Netflix released the docuseries “Big Vape: The Rise and Fall of JUUL,” which chronicled the founding of JUUL, its rise in popularity among youth, and the subsequent public backlash. The official Netflix YouTube channel posted a trailer promoting the docuseries and an official clip from the docuseries. Recent studies have demonstrated the utility of using comments posted under YouTube videos to analyze reactions to the content and discourse around the health topics explored in the video. Objective: We aimed to (1) systematically characterize nicotine and tobacco product (NTP)-related comments and replies posted in response to the docuseries trailer and video clip, and (2) explore integration of automated topic-modeling techniques with traditional human-generated qualitative coding. Methods: We extracted all comments and replies on the aforementioned YouTube clips one month after the docuseries release (n=532). Research assistants manually double-coded the comments using a systematically developed codebook. We also conducted an in-depth qualitative content analysis of all comments coded as potential misinformation. Simultaneously, we employed word clustering techniques including structural topic modeling to identify the overarching topics. Results: Of the 393 (73.8%) relevant comments, 250 (63.6%) expressed NTP sentiment with 42.8% of these (n=107) expressing pro-NTP sentiment and 18.4% (n=46) expressing complex sentiment. The most frequent content category was potential misinformation (27.5%, n= 108). These 108 comments contained 152 individual pieces of misinformation that broadly grouped within 6 themes. With topic modeling, we identified 9 topics that fell within 4 thematic categories. Conclusions: To the best of our knowledge, this is the first study to examine viewer reactions to the docuseries about JUUL. Our analysis of YouTube comments offers insight into current sentiment and misinformation regarding NTPs and highlights the potential utility of using mixed-methods to analyze NTP-related social media data.

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