Using social media data for e-cigarette surveillance and policy research

描述(由申请者提供)：社交媒体数据为公共卫生研究人员提供了前所未有的机会，让他们了解与酒精、烟草和其他药物(ATOD)相关的新兴使用模式、后果和背景因素。电子烟(电子烟)在不受监管的环境中迅速扩散，成年人和年轻人越来越多地使用电子烟，尽管其安全性的证据有限。这项拟议的研究调查了Twitter上分享的电子烟信息的性质，并将个人层面的Twitter数据与调查数据联系起来，以检查个人接触和分享这些信息的程度，以及社交媒体帖子是否反映了实际的看法和行为。结果将有助于我们对电子烟的理解，以及社交媒体数据用于监控的情况 以及对ATOD的监管。目标。目标1：使用文本挖掘和情感分析，描述在Twitter上共享的有关电子烟的信息，以了解(A)消费者的看法和使用；(B)广告商的营销策略，包括垃圾邮件行为；以及(C)改变联邦、州或地方法律。目标2：将个人层面的Twitter数据与自我报告的调查DAA联系起来，以(A)描述当前吸烟者和电子烟使用者可能接触和分享的电子烟信息的数量和类型，(B)评估信息暴露是否与召回和信息分享有关，以及(C)验证在Twitter上分享的信息是否反映了实际的看法和使用。目标1：关于电子烟的推文将使用迭代更新的搜索语法来识别，以捕捉电子烟不断变化的术语。文本挖掘和情感分析将使用监督和非监督算法进行。将使用检测推文行为、网络和帖子内容中的异常模式的计算方法来识别垃圾邮件或虚假消费者。在第一年开发的算法将在第二年和第三年每季度改进一次，以表征新兴主题。目的2：采用旋转小组设计，将招募1800名当前吸烟者和1800名电子烟和香烟双重使用者的全国便利性样本，以完成六项基线和后续调查，其中两项是侧重于政策或监管行动的快速反应调查。我们将分析受访者及其推特网络的公开推文，以及他们可能接触和分享的电子烟信息的范围和类型。这项调查将衡量参与者从他们的推特网络中回忆电子烟消息的情况，与电子烟相关的感知和使用，以及戒烟行为。我们将把Twitter数据与调查数据联系起来，以验证社交媒体帖子，并评估信息暴露是否与召回和信息共享相关，以及这些模式和关系如何因受访者的特征(例如，电子烟使用、戒烟意图、社交媒体使用)而变化。

DESCRIPTION (provided by applicant): Social media data give public health researchers unprecedented opportunity to understand emerging use patterns, consequences, and contextual factors related to alcohol, tobacco, and other drugs (ATOD). Electronic cigarettes (e-cigarettes) are rapidly proliferating in an unregulated environment and increasingly used by adults and youth despite limited evidence of their safety. The proposed study examines the nature of e-cigarette information shared on Twitter and links individual-level Twitter data to survey data to examine the extent to which individuals are exposed to and share this information and whether social media posts reflect actual perceptions and behaviors. Results will inform our understanding of e-cigarettes and the utility of social media data for surveillance and regulation of ATOD. Aims. Aim 1: Using text mining and sentiment analysis, characterize information about e-cigarettes shared on Twitter to understand (a) consumer perceptions and use; (b) advertisers' marketing strategies, including spamming behavior; and (c) changing federal, state, or local laws. Aim 2: Link individual-level Twitter data to self-reported survey daa to (a) describe the amount and type of e-cigarette information that current smokers and e-cigarette users are potentially exposed to and share, (b) assess whether information exposure is associated with recall and information sharing, and (c) validate whether information shared on Twitter reflects actual perceptions and use. Aim 1: Tweets about e-cigarettes will be identified using an iteratively updated search syntax to capture the changing terminology of e-cigarettes. Text mining and sentiment analysis will be conducted using supervised and unsupervised algorithms. Computational methods that detect abnormal patterns in tweeting behavior, networks, and post content will be employed to identify spamming or fake consumers. The algorithms developed in Year 1 will be refined quarterly in Years 2 and 3 to characterize emerging topics. Aim 2: Using a rotating panel design, a national convenience sample of 1,800 adult current smokers and 1,800 dual e-cigarette and cigarette users will be recruited to complete six baseline and follow-up surveys, with two being rapid response surveys focused on policy or regulatory action. We will analyze public Twitter posts of respondents and their Twitter networks and the extent and type of e-cigarette messages they were potentially exposed to and shared. The survey will measure participants' recall of e-cigarette messages from their Twitter network, e-cigarette-related perceptions and use, and cessation behaviors. We will link Twitter data to survey data to validate social media posts and assess whether information exposure is associated with recall and information sharing and how these patterns and relationships vary by respondent characteristics (e.g., e-cigarette use, quit intentions, social media use).