Using Innovative Machine Learning to Detect Organized Support and Opposition to E-cigarette Use Prevention Campaign Messaging on Twitter and TikTok

使用创新的机器学习来检测 Twitter 和 TikTok 上有组织的对电子烟使用预防运动消息的支持和反对

• 批准号: 10720700
• 负责人: Miao Feng
• 金额: $ 69.11万
• 依托单位: NATIONAL OPINION RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720700
• 关键词: Advanced Development; Advertising; Advocacy; Advocate; Antismoking; Attention; Attitude; Behavior; Brain; COVID-19 pandemic; Cigarette; Clutterings; Communication; Community Health; Confusion; Consensus; Consumption; Development; Devices; Diffuse Pattern; Diffusion; Effectiveness; Electronic Nicotine Delivery Systems; Electronic cigarette; Environment; Exposure to; Foundations; Freedom; Frequencies; Future; Gatekeeping; Harm Reduction; Health; Health behavior; Individual; Intervention; Linguistics; Machine Learning; Marketing; Measures; Media Campaign; Media Intervention; Misinformation; Nonprofit Organizations; Pattern; Policies; Prevalence; Prevention; Probability; Process; Public Health; Published Comment; Reporting; Research; Risk; Sales; Science; Semantics; Smoking; Social Network; Social isolation; Source; Technology; Teenagers; TikTok; Time; Tobacco; Tobacco Dependence; Tobacco use; Twitter; Youth; cigarette smoking; combat; combustible cigarette; community organizations; comparative; design; digital media; effectiveness evaluation; electronic cigarette use; health organization; improved; innovation; insight; non-cigarette tobacco product; peer; prevent; prevention effectiveness; programs; promoter; respiratory health; response; smoking cessation; social media; social structure; theories; tobacco control; tobacco products; tool development; vaping; young adult

Using Innovative Machine Learning to Detect Organized Support and Opposition to E-cigarette Use Prevention Campaign Messaging on Twitter and TikTok In recent years sharp increases in the consumption of non-cigarette tobacco products, including vaping products or electronic nicotine delivery systems (ENDS), have largely offset the significant decline in cigarette smoking rates in the US over the past five decades. In response, several state, local and federal tobacco control programs have developed prevention campaign messages for youth vaping and e-cigarette use. Some of these campaigns have also provoked significant opposition on social media from tobacco harm reduction advocates. Strong evidence supports the effectiveness of anti-smoking media campaigns in preventing youth cigarette smoking, but little research has examined the effects of campaigns that aim to prevent other types of tobacco use, such as anti-vaping campaigns. Further, previous evidence about prevention campaign effects did not take into account the current cluttered media environment, where campaign messages compete for audience attention with tobacco product promotion messages and both supportive and oppositional messaging about the campaigns themselves. Thus, social media interventions to prevent e-cigarette use are necessary but have unknown potential effects in a competitive communication environment characterized by an influx of ENDS product marketing and advocacy, potentially resulting in audience confusion and misinformation. Therefore, measuring engagement with e-cigarette use prevention campaigns on social media and understanding the patterns of such engagement and information flow represent important advancements in the science of prevention and lend evidential support for tobacco regulatory policy. The proposed project will assess the e-cigarette use prevention-related content amount, reach, and engagement on social media (Aim 1); identify sources and major themes associated with both supportive and oppositional messages using machine learning (Aim 2); and apply social network theories and approaches to examine patterns of social media users’ information seeking, exposure, dissemination, and gatekeeping in the campaign periods over time (Aim 3).

使用创新的机器学习来检测对电子烟使用的有组织的支持和反对 Twitter 和 TikTok 上的预防运动消息 近年来，包括电子烟​​在内的非卷烟烟草产品的消费急剧增加 产品或电子尼古丁传送系统（ENDS），在很大程度上抵消了卷烟销量的显着下降 过去五年美国的吸烟率对此做出了回应，一些州、地方和联邦烟草公司。 控制计划针对青少年吸电子烟和电子烟的使用制定了预防运动信息。 其中一些活动还在社交媒体上激起了人们对减少烟草危害的强烈反对 倡导者。 强有力的证据支持反吸烟媒体宣传活动在预防青少年吸烟方面的有效性 吸烟，但很少有研究考察旨在预防其他类型烟草的运动的影响 此外，之前没有关于预防活动效果的证据。 考虑到当前混乱的媒体环境，竞选信息争夺受众 关注烟草产品促销信息以及有关烟草产品的支持和反对信息 活动本身。 因此，防止电子烟使用的社交媒体干预措施是必要的，但潜力未知 竞争性传播环境中的影响，其特点是电子尼古丁传送系统（ENDS）产品营销的涌入和 宣传，可能会导致受众困惑和错误信息。 因此，衡量社交媒体上电子烟使用预防活动的参与度 了解这种参与和信息流的模式代表了 预防科学并为烟草监管政策提供证据支持。 评估社交媒体上与电子烟使用预防相关的内容数量、覆盖范围和参与度（目标 1); 确定与支持和反对信息相关的来源和主题 机器学习（目标 2）；并应用社交网络理论和方法来检查社交模式 媒体用户在竞选期间的信息寻求、曝光、传播和把关 时间（目标 3）。