Evidence map›Paper›PMID 39579342›Full record

ArticleNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2025

AI for Tobacco Control: Identifying Tobacco-Promoting Social Media Content Using Large Language Models.

Hüseyin Küçükali, Mehmet Sarper Erdoğan

Abstract read
In one paragraph

Article in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hüseyin KüçükaliCentre for Public Health, Queen's University Belfast, Belfast, UK.ORCID 0000-0003-1669-3107
Mehmet Sarper ErdoğanDepartment of Public Health, Istanbul University-Cerrahpasa, Istanbul, Türkiye.ORCID 0000-0003-2693-9916

Funding

Turkish Green Crescent Society 2019/7
6 · The paper itself

Abstract

introductionTobacco 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

methodsIn 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Artificial IntelligenceSmoking PreventionSocial MediaTobacco IndustryHumansLarge Language ModelsMarketingTobacco ControlTobacco ProductsTurkey

Identifiers

PMID39579342
PMCPMC12095795

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.