Mining the Social Web to Monitor Public Health and HIV Risk Behaviors

 风险行为和疾病的监测和监控是许多卫生相关机构和组织的首要任务，包括联合国艾滋病规划署、疾病控制和预防中心（CDC）以及当地公共卫生和流行病学部门。识别和定位风险行为和疾病的变化，可以通过提供关于哪些地方需要干预以及如何指导公共卫生工作的数据，大大改善公共卫生结果并减少与卫生有关的费用。例如，HIV研究人员、公共卫生部门和政府组织已经尝试识别和监测HIV风险行为（例如，性交和非法药物使用） 和艾滋病毒爆发，以改善预防和治疗工作，遏制日益严重的艾滋病毒流行。社交媒体的使用一直在迅速增加，这些技术的数据可能被用来识别艾滋病毒风险行为，如性和毒品相关的风险行为。尽管研究人员已经开发出使用社交媒体来监测艾滋病毒和公共卫生行为和结果的方法，但这些方法需要大量的人工时间，技术专业知识和多种软件平台来处理这些大数据。技术的进步，包括技术基础设施、数据挖掘和机器学习方法，可以用来提供工具，用于创建一个单一的自动化平台，用于提取自由文本社交媒体对话，标记这些对话以识别健康风险相关行为，并使用这些标签来监测疾病爆发。我们建议创建一个单一的自动化平台，收集社交媒体（Twitter）数据;识别，编码和标记提示HIV风险行为的推文;并提供HIV研究人员，公共卫生工作者和政策制定者可接受的输出，以监测HIV风险行为和结果。从这一应用程序开发的工具将是开放源代码的，专为流行病学家和公共卫生部门使用，并将可与其他软件工具集成，以提高公共卫生监测系统的有效性。

 DESCRIPTION (provided by applicant): Surveillance and monitoring of risk behavior and disease is a top priority of many health-related agencies and organizations, including the UN AIDS, Centers for Disease Control and Prevention (CDC), and local public health and epidemiology departments. Identification and localization of changes in risk behaviors and disease can dramatically improve public health outcomes and reduce health-related costs, by providing data on where interventions are needed and how to direct public health efforts. For example, HIV researchers, public health departments, and government organizations, have attempted to identify and monitor HIV risk behavior (e.g., sexual intercourse and illicit drug use) and HIV outbreaks to improve prevention and treatment efforts and curb a growing HIV epidemic. Social media use has been rapidly increasing, and data from these technologies might be leveraged for identification of HIV risk behaviors, such as sexual- and drug-related risk behaviors. Although researchers have developed methods of using social media to monitor HIV and public health behaviors and outcomes, these methods require extensive manual time, technical expertise, and multiple software platforms to process these big data. Advances in technology, including technology infrastructure, data mining, and machine learning approaches, can be leveraged to provide tools that can be used to create a single automated platform for extracting free-text social media conversations, labeling these conversations to identify health risk-related behaviors, and using these labels to monitor disease outbreaks. We propose to create a single automated platform that collects social media (Twitter) data; identifies, codes, and labels tweets that suggest HIV risk behaviors; and provides an output that is acceptable for HIV researchers, public health workers, and policymakers to monitor HIV risk behaviors and outcomes. The tools developed from this application will be open source, tailored for use for epidemiologists and public health departments, and will be available for integration with other software tools to improve the effectiveness of public health monitoring systems.