Building an Analytical Framework for Tobacco-Related Misinformation on Social Media: An Exploratory Analysis with Generative AI Assistance

Background The propagation of tobacco-related misinformation significantly impacts public health, particularly affecting people with less access to reliable information sources (such as those with lower education), who may also su affer disproportionate tobacco-related morbidity and mortality. This study analyzed a dataset from Twitter to identify the characteristics of tobacco-related misinformation, with the goal of creating a framework for its identification, categorization, and validation.

A collection of 3.4 million tweets related to tobacco and nicotine was refined to 842,754 after removing irrelevant and duplicate posts. LDA topic modeling identified six unique topics, from which two randomly selected samples of tweets were drawn to perform qualitative analysis and AI-assisted analysis to identify categories of tobacco misinformation.

The identified tobacco-related misinformation was categorized by three dimensions (1) content, including safety and health effects, cessation, substance, and policy; (2) type of falsehood, which included fabrication and unsubstantiated claims, misrepresentations, and distortions; and (3) source, ranging from individuals and retail stores to advocacy groups and influencers. A notable finding was the prevalence of policy-related discussions of tobacco misinformation on Twitter (X), highlighting this often-overlooked domain. The controversy over vaping has amplified pro-vaping voices on social media, with content frequently misinterpreting scientific findings, policies, and expert opinions, reflecting more nuanced and difficult to recognize falsehood in the misleading content.

This study offers a comprehensive framework for analyzing tobacco-related misinformation on social media, emphasizing key issues in policy debates and the presence of conspiracy narratives. This framework can inform the design of interventions for less informed populations and enhance data annotation for machine learning tasks. - E-cigarettes - misinformation - tobacco control - social media Competing Interest Statement The authors have declared no competing interest. Funding Statement Research reported in this study was supported by the University of California Tobacco Related Disease Research Program, under the award T33FT6729, and the National Cancer Institute of the National Institutes of Health award R01CA283038. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Source data were publicly available before the initiation of the study and can be found through visiting the website of X (formerly Twitter). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Funding Declaration Research reported in this study was supported by the University of California Tobacco Related Disease Research Program, under the award T33FT6729, and the National Cancer Institute of the National Institutes of Health award R01CA283038. Declaration of interest statement The authors have no conflict of interests to declare. Human Ethics and Consent to Participate declarations: not applicable Data Availability All data produced are available online at X.com