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

使用社交媒体数据进行电子烟监测和政策研究

• 批准号: 9121486
• 负责人: Annice E Kim
• 金额: $ 43.13万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-24 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9121486
• 关键词: Address; Adult; Advertising; Aerosols; Alcohol or Other Drugs use; Alcohols; Algorithms; Area; Attitude; Baseline Surveys; Behavior; Characteristics; Cigarette; Code; Communication; Computing Methodologies; Data; Data Set; Detection; Electronic cigarette; Ensure; Environment; Grana; Health; Human; Individual; Industry; Laws; Lead; Link; Marketing; Measures; Methods; Nature; Nicotine; Participant; Patient Self-Report; Pattern; Perception; Pharmaceutical Preparations; Policies; Policy Research; Population Surveillance; Principal Investigator; Proliferating; Public Health; Recruitment Activity; Regulation; Research; Research Personnel; Respondent; Safety; Sampling; Smoker; Social Network; Stream; Surveillance Methods; Surveys; Technology; Terminology; Text; Time; Tobacco; Training; United States Food and Drug Administration; Update; Vendor; Youth; cohort; contextual factors; data mining; design; follow-up; improved; innovation; insight; multidisciplinary; pattern perception; response; social media; spam; surveillance data; syntax; text searching; tobacco control; young adult

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).

描述（由申请人提供）：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来了解与酒精、烟草和其他药物 (ATOD) 相关的新兴使用模式、后果和背景因素。尽管其安全性证据有限，但电子烟在不受监管的环境中迅速扩散，并越来越多地被成年人和青少年使用。拟议的研究调查了 Twitter 上共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。结果将有助于我们了解电子烟以及社交媒体数据用于监控的效用 ATOD 的监管。目标。目标 1：使用文本挖掘和情感分析，描述 Twitter 上分享的电子烟信息的特征，以了解 (a) 消费者的看法和使用情况； (b) 广告商的营销策略，包括垃圾邮件行为； (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用户可能接触和分享的电子烟信息的数量和类型，(b) 评估信息是否曝光与回忆和信息共享相关，并且 (c) 验证 Twitter 上共享的信息是否反映了实际的感知和使用。目标 1：将使用迭代更新的搜索语法来识别有关电子烟的推文，以捕获不断变化的电子烟术语。将使用监督和无监督算法进行文本挖掘和情感分析。检测推文行为、网络和帖子内容中异常模式的计算方法将用于识别垃圾邮件或虚假消费者。第一年开发的算法将在第二年和第三年每季度进行一次完善，以描述新兴主题的特征。目标 2：采用旋转面板设计，将招募 1,800 名当前成年吸烟者和 1,800 名电子烟和卷烟双重使用者的全国便利样本，以完成六项基线和后续调查，其中两项是针对政策或政策的快速反应调查监管行动。我们将分析受访者的公开 Twitter 帖子及其 Twitter 网络，以及他们可能接触和分享的电子烟消息的程度和类型。该调查将衡量参与者对推特网络上的电子烟信息的回忆、电子烟相关的认知和使用以及戒烟行为。我们将 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回忆和信息共享相关，以及这些模式和关系如何根据受访者特征（例如电子烟使用、戒烟意图、社交媒体使用）而变化。 。

项目成果

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.

用受访者 Twitter 数据补充调查，以衡量电子烟信息暴露程度。

• 发表时间: 2019
• 作者: Murphy, Joe;Hsieh, Y Patrick;Wenger, Michael;Kim, Annice E;Chew, Rob
• 通讯作者: Chew, Rob

Predicting age groups of Twitter users based on language and metadata features.

根据语言和元数据特征预测 Twitter 用户的年龄组。

• 发表时间: 2017
• 影响因子: 3.7
• 作者: Morgan;Kim, Annice E;Chew, Robert F;Ruddle, Paul
• 通讯作者: Ruddle, Paul

Classification of Twitter Users Who Tweet About E-Cigarettes.

发布有关电子烟的推文的 Twitter 用户的分类。

• 发表时间: 2017-09-26
• 作者: Kim, Annice;Miano, Thomas;Chew, Robert;Eggers, Matthew;Nonnemaker, James
• 通讯作者: Nonnemaker, James

Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers.

比较 Twitter 和在线调查招募电子烟用户和吸烟者的小组。

• 发表时间: 2016-11-15
• 影响因子: 7.4
• 作者: Guillory, Jamie;Kim, Annice;Murphy, Joe;Bradfield, Brian;Nonnemaker, James;Hsieh, Yuli
• 通讯作者: Hsieh, Yuli

Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets.

Lifelong-RL：终身松弛标签，用于分离意见目标中的实体和方面。

• 发表时间: 2016-11
• 作者: Shu, Lei;Liu, Bing;Xu, Hu;Kim, Annice
• 通讯作者: Kim, Annice