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对电子烟使用的有组织的支持和反对 Twitter和TikTok上的预防活动信息 近年来，非卷烟烟草产品的消费急剧增加，包括vaping 产品或电子尼古丁输送系统（ENDS），在很大程度上抵消了香烟的显着下降， 美国过去50年的吸烟率。作为回应，几个州，地方和联邦烟草 控制方案已经为青年使用电子烟和电子烟制定了预防运动信息。一些 这些运动也在社交媒体上引起了烟草危害减少的强烈反对 律师 强有力的证据支持反吸烟媒体运动在防止青少年吸烟方面的有效性 吸烟，但很少有研究调查旨在防止其他类型烟草的运动的影响 使用，如反vaping运动。此外，以前关于预防运动效果的证据并没有 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，以及支持和反对烟草产品的信息。 运动本身。 因此，社交媒体干预以防止电子烟的使用是必要的，但其潜力未知 在竞争激烈的通信环境中的影响，其特点是ENDS产品营销的涌入， 宣传，可能导致受众混淆和错误信息。 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着在 为烟草管制政策提供证据支持。拟议项目将 评估电子烟使用预防相关内容的数量、覆盖范围和社交媒体上的参与度（Aim 1）;使用以下方法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）;并应用社会网络理论和方法来研究社会模式 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）。

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).