Mining real-time social media big data to monitor HIV: Development and Ethical Issues

社交“大数据”包含的信息对解决艾滋病护理相关问题有着广泛的影响 连续体。社交大数据是指来自社交媒体和在线平台的信息，其中个人 社区创建、共享和讨论内容。全世界每四个人中就有一个，或者说超过十亿人， 在这些网站上公开记录他们的活动、意图、情绪、意见和社交互动。 他们正在以越来越大的数量和速度做到这一点，包括每天在Twitter和 每天在Facebook上分享47.5亿个内容项目。随着这些平台的数量不断增加 社交大数据分析支持访问公开可用的用户数据，是一种很有前途的新方法 对可用于监测和预测现实世界公共卫生的行为进行有机观察 问题，如艾滋病毒发病率。因此，需要社交数据等新工具来补充现有的 艾滋病毒数据收集方法。 在初步研究中，我们团队开发了第一种识别心理和行为的方法 社交大数据(&gt；5.5亿条推文)中发现的与艾滋病毒诊断相关的特征。自.以来 感染艾滋病毒风险最高的群体(如少数群体)是增长最快的推特用户，以及 由于社交媒体用户被发现公开分享个人信息，我们确定和 收集建议艾滋病毒危险行为(例如，吸毒、高危性行为等)的推文并建立了模型 他们与疾控中心关于艾滋病毒诊断的统计数据一起。我们发现艾滋病毒-- 相关推文和县级艾滋病病例，控制社会经济状况指标等变量。 问题是，这种方法目前还不能扩展到艾滋病毒研究人员和公共卫生部门使用 组织。尽管公共卫生机构对挖掘社会数据来解决艾滋病毒问题感兴趣，但目前 大多数健康科学家无法使用这些工具，因为这些工具需要高级计算机科学专业知识。 例如，每天分析5亿条推文需要大数据工程、先进机器方面的专业知识 学习、自然语言处理和人工智能。开发用于挖掘社交网络的单一平台 由艾滋病毒研究人员和为艾滋病毒研究人员设计和测试的数据可能会对艾滋病毒产生重大影响 预防、检测和治疗。我们寻求创建一个收集社交媒体的单一自动化平台 数据；识别、编码和标记建议艾滋病毒相关行为的推文；并最终预测区域 艾滋病毒发病率。由于与挖掘人们的数据相关的潜在伦理问题，我们还寻求 采访当地和区域艾滋病毒组织的工作人员和受艾滋病毒影响的参与者，以获得他们的观点 关于与这种方法相关的伦理问题。从该应用程序开发的软件将是 与艾滋病毒研究人员和医护人员共享，以提供可用于抗击 艾滋病毒的传播。

Social “big data” holds information with wide-ranging implications for addressing issues along the HIV care continuum. Social big data refers to information from social media and online platforms on which individuals and communities create, share, and discuss content. One in four people worldwide, or over a billion people, are publically documenting their activities, intentions, moods, opinions, and social interactions on these sites. They are doing so with increasing volume and velocity, including 400 million “tweets” per day on Twitter and 4.75 billion content items shared per day on Facebook. With an increasing number of these platforms supporting access to publicly-available user data, social big data analysis is a promising new approach for attaining organic observations of behavior that can be used to monitor and predict real-world public health problems, such as HIV incidence. New tools such as social data are therefore needed to supplement existing HIV data collection methods. In preliminary research, our team developed the first approach that identifies psychological and behavioral characteristics from social big data (>550 million tweets) found to be associated with HIV diagnoses. Since groups at the highest risk for HIV (e.g., minority populations) are the fastest growing Twitter users, and because social media users have been found to publicly share personal information, we identified and collected tweets suggesting HIV risk behaviors (e.g., drug use, high-risk sexual behaviors, etc.) and modeled them alongside CDC statistics on HIV diagnoses. We found a significant positive relationship between HIV- related tweets and county-level HIV cases, controlling for socioeconomic status measures and other variables. The problem is that this approach is not currently scalable for use by HIV researchers and public health organizations. Although public health agencies are interested in mining social data to address HIV, current tools are not accessible to most health scientists, as the tools require advanced computer science expertise. For example, analyzing 500 million tweets a day requires expertise in big data engineering, advanced machine learning, natural language processing, and artificial intelligence. Developing a single platform for mining social data that has been designed and tested by and for HIV researchers could provide a significant impact on HIV prevention, testing, and treatment. We seek to create a single automated platform that collects social media data; identifies, codes, and labels tweets that suggest HIV-related behaviors; and ultimately predicts regional HIV incidence. Because of the potential ethical issues associated with mining people's data, we also seek to interview staff at local and regional HIV organization and participants affected by HIV to gain their perspectives on the ethical issues associated with this approach. The software developed from this application will be shared with HIV researchers and health care workers to provide additional tools that can be used to combat the spread of HIV.