权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

挖掘实时社交媒体大数据来监测艾滋病毒：发展和道德问题

基本信息

批准号：
9903175
负责人：
Sean Young
金额：
$ 73.71万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9903175
关键词：
AIDS prevention Address Affect Algorithms Artificial Intelligence Behavior Behavioral Benefits and Risks Big Data Big Data Methods Centers for Disease Control and Prevention (U.S.)Characteristics Code Communities Computer software Computers Continuity of Patient Care County Data Data Analyses Data Collection Data Engineering Development Drug usage Epidemiology Ethical Issues Ethics Facebook HIV HIV diagnosis HIV risk Hand Health Health Personnel Human Incidence Individual Informed Consent Infrastructure Interview Label Learning Letters Machine Learning Manuals Measures Methods Mining Minority Modeling Monitor Moods Natural Language Processing Outcome Participant Population Privacy Public Health Research Research Personnel Risk Risk Behaviors Scientist Site Social Characteristics Social Interaction Socioeconomic Status Structure Technology Testing Text Time Twitter Work behavior observation combat computer science design health organization high risk high risk sexual behavior insight interest machine learning algorithm novel strategies psychologic public health priorities social social media software development software infrastructure statistics tool

项目摘要

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.

社会“大数据”包含的信息对解决艾滋病毒护理沿着的问题具有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，和社区创建、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，在这些网站上记录他们的活动、意图、情绪、观点和社会互动。他们这样做的数量和速度越来越快，包括每天在Twitter上发布4亿条“推文”， 4.75每天在Facebook上分享10亿条内容。随着这些平台的不断增加，支持访问公开的用户数据，社交大数据分析是一种有前途的新方法，获得可用于监测和预测现实世界公共卫生的行为的有机观察比如艾滋病的发病率。因此，需要社会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，我们的团队开发了第一种方法，从社交大数据（> 5.5亿条推文）中发现与艾滋病毒诊断相关的特征。以来艾滋病毒感染风险最高的群体（例如，少数族裔人口）是增长最快的Twitter用户，由于社交媒体用户被发现公开分享个人信息，我们发现，收集的提示HIV风险行为的推文（例如，吸毒、高危性行为等）和建模与CDC关于艾滋病诊断的统计数据一起。我们发现艾滋病病毒与艾滋病之间存在显著的正相关- 相关推文和县级艾滋病毒病例，控制社会经济地位措施和其他变量。问题是这种方法目前还无法扩展供艾滋病毒研究人员和公共卫生人员使用组织的虽然公共卫生机构有兴趣挖掘社会数据来解决艾滋病毒问题，但目前大多数卫生科学家无法获得这些工具，因为这些工具需要先进的计算机科学专门知识。例如，每天分析5亿条推文需要大数据工程方面的专业知识，先进的机器学习、自然语言处理和人工智能。开发一个单一的平台来挖掘社交由艾滋病毒研究人员设计和测试的数据可以对艾滋病毒产生重大影响预防、检测和治疗。我们寻求创建一个单一的自动化平台，数据;识别，编码和标签提示艾滋病毒相关行为的推文;并最终预测区域艾滋病毒感染率。由于与挖掘人们的数据相关的潜在道德问题，我们还寻求与当地和区域艾滋病毒组织工作人员和受艾滋病毒影响的参与者进行访谈，以了解他们的观点与这种方法相关的道德问题。从该应用程序开发的软件将是与艾滋病毒研究人员和卫生保健工作者分享，以提供更多的工具，艾滋病毒的传播。