Leveraging Twitter to Monitor Nicotine and Tobacco Cancer Communication

Twitter数据的模式已经彻底改变了对流感爆发等公共卫生事件的理解。 虽然研究人员已经开始研究Twitter上与物质使用有关的消息，但该项目将 加强使用Twitter作为一种信息工具，更严格地检查尼古丁，烟草和癌症- 相关通信。Twitter特别适合这项工作，因为它的用户通常是青少年， 年轻人，种族和少数民族，所有这些人都有增加尼古丁和烟草的风险 产品（NTP）的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，从而能够进行大规模的系统研究。所以我们的 多学科的专家团队在不同的相关领域，包括公共卫生，行为科学， 计算语言学，计算机科学，生物医学信息学，信息隐私和安全-意志 建立在我们以前的研究，开发和验证结构化算法，提供自动化的 监控Twitter上与NTPs相关的多方面和不断变化的信息。一是 定性评估特定编码变量的相关NTP相关推文的分层随机样本， 诸如消息的主要情感和其他具有潜在价值的关键信息（例如，是否 信息涉及购买/销售，政策/法律和癌症相关的沟通）。Twitter将获得 直接从Twitter使用我们开发的软件，利用Twitter优化的全面列表， 搜索与NTP相关的字符串。第二，我们将从统计学上确定哪些消息特征（例如，的 某些单词、标点符号和/或结构的存在）与每个编码的 每个搜索字符串的变量。利用这些信息，我们将创建专门的机器学习（ML） 算法基于最先进的方法，从自然语言处理（NLP）到自动 对未来的Twitter数据进行评估和分类。第三，我们将使用这些信息提供自动评估 当前和未来的数据流。使用季节性自回归积分移动的时间序列分析 ARIMA将确定与以下内容相关的消息传递量是否随时间发生重大变化： 每个特定的感兴趣的编码变量。趋势将在每日、每周和每月级别进行检查， 因为每一层都有潜在的干预价值。为了最大限度地发挥其转化价值 项目，我们将与公共卫生部门的利益相关者谁是精简专家合作 传播可行的趋势数据。总之，这个项目将大大促进我们的理解， NTPs在社交媒体上的代表性，以及我们进行自动监控和 分析这一内容。该项目将产生重要和具体的成果，包括开放源码 未来研究人员和流程的算法，以快速传播可操作的数据，为定制社区- 水平干预。

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.