Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 10, 2025
Date Accepted: Oct 28, 2025
An exploratory typology of tobacco-related misleading content on social media: a qualitative analysis of Instagram and TikTok
ABSTRACT
Background:
Tobacco-related misinformation on social media platforms presents growing challenges to digital health communication and public health. While prior studies have focused on platform-specific patterns, a unified framework for categorizing and comparing misinformation across platforms is lacking. Such a framework is essential for improving infodemiological surveillance and designing targeted digital interventions.
Objective:
This study aimed to develop a cross-platform taxonomy to categorize tobacco-related misinformation on X (formerly Twitter), Instagram, and TikTok.
Methods:
We analyzed a total of 4,850 Instagram posts using a combination of generative AI and human validation by two independent reviewers. Additionally, 719 TikTok videos were coded manually using qualitative content analysis. Data were collected between January 2020 and August 2023. Selection criteria ensured relevance and quality of included posts. Categories previously developed for Twitter were integrated and refined through an iterative process involving reviewer discussion and adjudication by a third expert reviewer.
Results:
The resulting taxonomy consists of five core archetypes of misinformation: 1) False or misleading health claims, 2) wellness and lifestyle appeal, 3) conspiracy-driven policy agenda, 4) undermining trust in science and medicine, and 5) recreational nicotine use normalization. Each archetype is annotated with types of false claims and source attributes. The taxonomy provides a structured lens to view how misinformation is tailored to digital environments and target audiences.
Conclusions:
This cross-platform taxonomy supports digital health research by integrating AI and qualitative methods to systematically categorize tobacco-related misinformation. It can inform the development of automated misinformation detection models, enhance real-time infodemiological monitoring, and guide digital public health campaigns. By identifying narrative strategies and misinformation actors, the taxonomy contributes to scalable, targeted, and context-sensitive counter-messaging in digital health ecosystems.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.