Leveraging Twitter to Monitor Nicotine and Tobacco Cancer Communication

利用 Twitter 监控尼古丁和烟草癌症的交流

• 批准号: 10214567
• 负责人: Brian A. Primack
• 金额: $ 11.25万
• 依托单位: UNIVERSITY OF ARKANSAS AT FAYETTEVILLE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2022-07-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214567
• 关键词: Adolescent; Affect; Algorithms; Attitude; Behavioral; Behavioral Sciences; Cancer Control; Categories; Characteristics; Cigarette; Code; Collaborations; Communication; Communities; Complex; Computational Linguistics; Computational algorithm; Computer software; Computers; County; Data; Disease Outbreaks; Electronic cigarette; Epidemiologic Methods; Event; Food; Football game; Future; Gold; Health; Health Care Costs; Individual; Influenza A Virus, H1N1 Subtype; Intervention; Laws; Linguistics; Literature; Malignant Neoplasms; Marketing; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Names; Natural Language Processing; Nicotine; Outcome; Pattern; Policies; Privacy; Process; Public Health; Public Opinion; Research; Research Personnel; Resources; Retrieval; Risk; Sampling; Scientist; Security; Specificity; Structure; Techniques; Testing; Time; Time Series Analysis; Tobacco; Tobacco use; Twitter; Work; automated analysis; base; biomedical informatics; cancer prevention; computer program; computer science; computerized tools; data standards; data streams; ethnic minority population; geographic difference; hookah; improved; influenza outbreak; interest; machine learning algorithm; mortality; multidisciplinary; nicotine use; novel; open source; phrases; prospective; racial minority; social; social media; software development; statistics; substance use; time use; tobacco products; tool; trend; vaping; young adult

Patterns in Twitter data have revolutionized understanding of public health events such as influenza outbreaks. While researchers have begun to examine messaging related to substance use on Twitter, this project will strengthen the use of Twitter as an infoveillance tool to more rigorously examine nicotine, tobacco, and cancer- related communication. Twitter is particularly suited to this work because its users are commonly adolescents, young adults, and racial and ethnic minorities, all of whom are at increased risk for nicotine and tobacco product (NTP) use and related health consequences. Additionally, due to the openness of the platform, searches are replicable and transparent, enabling large-scale systematic research. Therefore, our multidisciplinary team of experts in diverse relevant fields—including public health, behavioral science, computational linguistics, computer science, biomedical informatics, and information privacy and security—will build upon our previous research to develop and validate structured algorithms providing automated surveillance of Twitter’s multifaceted and continuously evolving information related to NTPs. First, we will qualitatively assess a stratified random sample of relevant NTP-related tweets for specific coded variables, such as the message’s primary sentiment and other key information of potential value (e.g., whether a message involves buying/selling, policy/law, and cancer-related communication). Tweets will be obtained directly from Twitter using software we developed that leverages a comprehensive list of Twitter-optimized search strings related to NTPs. Second, we will statistically determine what message characteristics (e.g., the presence of certain words, punctuation, and/or structures) are most strongly associated with each of the coded variables for each search string. Using this information, we will create specialized Machine Learning (ML) algorithms based on state-of-the-art methods from Natural Language Processing (NLP) to automatically assess and categorize future Twitter data. Third, we will use this information to provide automatic assessment of current and future streaming data. Time series analyses using seasonal Auto-Regressive Integrated Moving Averages (ARIMA) will determine if there are significant changes over time in volume of messaging related to each specific coded variables of interest. Trends will be examined at the daily, weekly, and monthly level, because each of these levels is potentially valuable for intervention. To maximize the translational value of this project, we will partner with public health department stakeholders who are experts in streamlining dissemination of actionable trends data. In summary, this project will substantially advance our understanding of representations of NTPs on social media—as well as our ability to conduct automated surveillance and analysis of this content. This project will result in important and concrete deliverables, including open-source algorithms for future researchers and processes to quickly disseminate actionable data for tailoring community- level interventions.

Twitter数据中的模式已彻底改变了人们对公共卫生事件（例如影响力爆发）的理解。 尽管研究人员已经开始检查Twitter上与药物使用有关的消息传递，但该项目将 加强将Twitter用作不受欢迎的工具，以更严格地检查尼古丁，烟草和癌症 - 相关沟通。 Twitter特别适合这项工作，因为其用户通常是青少年， 年轻人以及种族和少数族裔，他们的尼古丁和烟草风险增加 产品（NTP）使用和相关的健康后果。此外，由于平台的开放性， 搜索是可复制和透明的，可以实现大规模的系统研究。因此，我们的 潜水领域专家的多学科团队 - 包括公共卫生，行为科学， 计算语言学，计算机科学，生物医学信息以及信息隐私和安全性 - 将会 基于我们先前的研究，以开发和验证提供自动化的结构化算法 对Twitter的多方面且不断发展的信息的监视与NTP有关。首先，我们会的 定性评估针对特定编码变量的相关NTP相关推文的分层随机样本， 例如消息的主要情感和潜在价值的其他关键信息（例如，是否a 消息涉及买入/销售，政策/法律和与癌症有关的沟通）。推文将获得 直接从Twitter使用软件，我们开发了一个充分利用Twitter优化的列表 搜索字符串与NTP有关。其次，我们将从统计上确定哪些消息特征（例如 某些单词，标点符号和/或结构的存在与每个编码都最密切相关 每个搜索字符串的变量。使用此信息，我们将创建专业的机器学习（ML） 基于自然语言处理（NLP）到自动的最新方法的算法 评估并分类未来的Twitter数据。第三，我们将使用此信息提供自动评估 当前和将来的流数据。时间序列分析使用季节性自动回归整合运动 平均值（Arima）将确定随着时间的流逝，与 每个感兴趣的特定编码变量。趋势将在每日，每周和每月的水平上进行检查， 因为这些级别中的每个级别都可能对干预有价值。为了最大化这一点的翻译价值 项目，我们将与精简专家的公共卫生部门利益相关者合作 传播可行的趋势数据。总而言之，该项目将大大提高我们的理解 NTP在社交媒体上的代表 - 以及我们进行自动监视和 该内容的分析。该项目将导致重要和具体的可交付成果，包括开源 未来研究人员和流程的算法，以快速传播可行的数据以量身定制社区 - 水平干预措施。