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

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Hüseyin Küçükali, Mehmet Sarper Erdoğan
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引用次数: 0

Abstract

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.

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 and predicted tobacco promotion in all tweets with 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-address 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. Further, 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.

烟草控制的人工智能:使用大型语言模型识别烟草宣传社交媒体内容。
导言:烟草公司利用社交媒体绕过营销限制。研究表明,接触社交媒体上的烟草促销会影响随后的吸烟行为,但要监控此类内容却很困难。我们开发了一种人工智能,可以自动识别社交媒体上的烟草促销内容:在这项混合方法研究中,我们收集了 177,684 条土耳其语 Twitter 上发布的烟草相关推文。通过对 200 条推文样本进行归纳内容分析,确定了社交媒体上宣传烟草的主要机制。然后,根据这些机制对 5000 条推文样本进行演绎分析和标记。使用标注的数据集对预先训练的基于转换器的大型语言模型进行了微调,并使用该模型预测了所有推文中的烟草促销行为:结果:社交媒体上烟草促销的主要机制包括行为建模、表达积极态度、推荐使用以及营销品牌或供应商。所开发的模型可识别出社交媒体中的烟草促销内容,召回率为 87.8%,精确率为 81.1%。对一个月内推文中烟草促销内容的分析表明了该模型的实用性:该工具使监控社交媒体中的烟草促销成为可能,并为烟草控制政策和实践创造了新的机会,不仅在监控和执法方面,而且在健康促进方面:由于社交媒体的特性,社交媒体上的烟草促销是一个众所周知却又难以解决的问题。本研究利用最先进的人工智能方法--大型语言模型,自动、精确地识别社交媒体内容中的烟草促销。与之前提出的技术相比,所开发的模型具有更好的预测性能。这项研究可对烟草促销内容进行监控,既可用于研究目的,也可用于执行烟草控制措施。此外,我们还提出了一系列健康促进机会,包括发展个人技能、创造支持性环境和加强社区行动等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nicotine & Tobacco Research
Nicotine & Tobacco Research 医学-公共卫生、环境卫生与职业卫生
CiteScore
8.10
自引率
10.60%
发文量
268
审稿时长
3-8 weeks
期刊介绍: Nicotine & Tobacco Research is one of the world''s few peer-reviewed journals devoted exclusively to the study of nicotine and tobacco. It aims to provide a forum for empirical findings, critical reviews, and conceptual papers on the many aspects of nicotine and tobacco, including research from the biobehavioral, neurobiological, molecular biologic, epidemiological, prevention, and treatment arenas. Along with manuscripts from each of the areas mentioned above, the editors encourage submissions that are integrative in nature and that cross traditional disciplinary boundaries. The journal is sponsored by the Society for Research on Nicotine and Tobacco (SRNT). It publishes twelve times a year.
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