2024/11/23 by Hüseyin Küçükali, Mehmet Sarper Erdoğan · 1 voice
Medicine · Social Sciences · Health Professions · #Smoking Behavior and Cessation #Social Media in Health Education #Health Literacy and Information Accessibility
paper · pdf · doi:10.1093/ntr/ntae276
openalex publication_date 2024/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
INTRODUCTION: Tobacco companies use social media to bypass marketing restrictions. Studies show that exposure to tobacco promotion on social media influences subsequent smoking behavior, yet it is challenging to monitor such content. We developed an artificial intelligence that can automatically identify tobacco-promoting content on social media. AIMS AND METHODS: In this mixed methods study, 177,684 tobacco-related tweets published on Twitter in Turkish were collected. Through inductive content analysis of a sample of 200 tweets, the main mechanisms by which tobacco is promoted on social media were identified. Then, a sample of 5000 tweets was deductively analyzed and labeled based on those mechanisms. A pre-trained transformer-based Large Language Model was fine-tuned using the labeled dataset. Then, tobacco promotion in all tweets was predicted using this model. RESULTS: The main mechanisms of tobacco promotion on social media included modeling the behavior, expressing positive attitudes, recommending use, and marketing brands or vendors. The developed model identified tobacco-promoting social media content with 87.8% recall and 81.1% precision. The utility of the model was demonstrated in the analysis of tobacco promotion in tweets for a period of a month. CONCLUSIONS: This tool makes it possible to monitor tobacco promotion in social media and creates new opportunities for tobacco control policy and practice, not only in surveillance and enforcement but also in health promotion. IMPLICATIONS: Tobacco promotion in social media is a well-known yet hard-to-addressed problem due to the nature of social media. This study leverages a cutting-edge AI approach, Large Language Models, to identify tobacco promotion in social media content automatically and precisely. The developed model offers better prediction performance than previously proposed techniques. The study enables surveillance of tobacco-promoting content both for research purposes and enforcement of tobacco control measures. Furthermore, we suggest a range of health promotion opportunities this tool can help with from developing personal skills to creating supportive environments and strengthening community actions.