ArticleJournal of medical Internet research2025
An Exploratory Typology of Tobacco-Related Misleading Content on Social Media: Qualitative Analysis of Instagram and TikTok.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- When Risk Is Invisible: Alcohol-Cancer Communication on Social Media.Alcohol, clinical & experimental research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundTobacco-related misinformation on social media platforms presents growing challenges to digital health communication and public health. Although 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.
objectiveThis study was an exploratory analysis aimed to build a cross-platform typology to categorize tobacco-related misinformation.
methodsData from Instagram and TikTok between January 2020 and August 2023 were collected using a third-party data collection platform (CrowdTangle) and the TikTok Research application programming interface (API). We reviewed a total of 4850 Instagram posts using a combination of generative artificial intelligence (AI) and human validation by two independent reviewers. In addition, 719 TikTok videos were reviewed manually using qualitative analysis. We iteratively developed and refined the exploratory typology informed by the literature integrating our prior analysis of Twitter data and these new datasets.
resultsOf the 22 (71%) Instagram posts and 9 (29%) TikTok videos we analyzed closely to classify misinformation, 2 (6.5%) were about cigarettes, 22 (71%) were about electronic cigarettes (e-cigarettes), 1 (3.2%) was about heated tobacco products (HTPs), 2 (6.5%) were about nicotine (not mentioning specific products), and 3 (9.7%) were about cannabidiol (CBD) products. 1 (3.2%) post did not mention any type of products. These categories could overlap in a single post. The resulting typology consisted of five core narrative archetypes: false or misleading health claims (A1), wellness and lifestyle appeal (A2), conspiracy-driven policy agenda (A3), undermining trust in science and medicine (A4), and recreational nicotine use normalization (A5). Each archetype has attributes of false claim types and sources. Among the posts we analyzed, A1 and A2 were most likely to be found on Instagram. A3 was most frequently found on Twitter. A4 was commonly seen on both Twitter and TikTok, and A5 was most frequently found on TikTok. Two additional dimensions-type of falsehood and source-were also added to characterize a given misinformation post. This exploratory typology paved the way for a structured lens to view how misinformation is tailored to digital environments and target audiences.
conclusionsThis cross-platform typology building supports digital health research by integrating AI and qualitative methods to 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 to build tailored countermessaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.