Leveraging Twitter to Monitor Nicotine and Tobacco Cancer Communication

Twitter 数据的模式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 上与药物使用相关的消息传递，但该项目将 加强 Twitter 作为信息监视工具的使用，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，因为它的用户通常是青少年， 年轻人以及少数族裔和族裔，所有这些人接触尼古丁和烟草的风险都较高 产品（NTP）的使用和相关的健康后果。此外，由于平台的开放性， 搜索是可复制且透明的，可以实现大规模的系统研究。因此，我们的 由不同相关领域的多学科专家组成的团队，包括公共卫生、行为科学、 计算语言学、计算机科学、生物医学信息学以及信息隐私和安全——将 基于我们之前的研究来开发和验证结构化算法，提供自动化 监视 Twitter 与 NTP 相关的多方面且不断变化的信息。首先，我们将 定性评估相关 NTP 相关推文的分层随机样本的特定编码变量， 例如消息的主要情绪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/法律以及与癌症相关的沟通）。将获得推文 使用我们开发的软件直接从 Twitter 获取，该软件利用了 Twitter 优化的全面列表 搜索与 NTP 相关的字符串。其次，我们将统计确定哪些消息特征（例如， 某些单词、标点符号和/或结构的存在）与每个编码的关联性最强 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) 基于从自然语言处理 (NLP) 到自动化的最先进方法的算法 评估和分类未来的 Twitter 数据。第三，我们将使用这些信息来提供自动评估 当前和未来的流数据。使用季节性自回归积分移动进行时间序列分析 平均值 (ARIMA) 将确定与以下内容相关的消息传递量是否随时间发生显着变化： 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， 因为每个级别对于干预都有潜在的价值。为了最大限度地发挥这一成果的转化价值 项目中，我们将与公共卫生部门的利益相关者合作，他们是精简方面的专家 传播可操作的趋势数据。总之，这个项目将极大地增进我们的理解 NTP 在社交媒体上的表现，以及我们进行自动监控和处理的能力 对此内容的分析。该项目将产生重要且具体的可交付成果，包括开源 未来研究人员和流程的算法可以快速传播可操作的数据以定制社区- 级干预。

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.