Utilizing social media as a resource for mental health surveillance

描述(申请人提供)：严重抑郁障碍是美国最常见的衰弱疾病之一，终生患病率为16.2%。目前，全国范围内的心理健康监测采取大规模电话调查的形式。这些调查的运行成本很高，需要人工电话接线员团队。即使是最大的系统，行为风险因素监测系统，也只覆盖了0.13%的美国人口。Twitter(和其他微博服务)为公共卫生监测提供了丰富的、但也是简洁的多语种实时数据来源。自然语言处理(NLP)提供了从文本中“解锁”数据的技术和资源。我们建议使用Twitter和NLP作为一种经济有效和灵活的方法，以增强当前基于电话的监测方法，用于人群水平的抑郁症监测。 这项拨款申请有两个主要方面。首先，调查使用Twitter数据进行公共卫生监督时出现的伦理问题和对隐私的挑战(目标一)。第二，从推特上开发用于精神疾病实时公共卫生监测的技术和资源(目标二和目标三)。Aim One寻求通过与Twitter用户直接接触来调查和编纂我们作为研究人员对Twitter用户的责任。目标二，我们将构建和评估自然语言处理资源--算法、词典和分类--以支持识别Twitter数据中的抑郁症状。对于目标三，我们将构建和评估自然语言处理模块和服务，这些模块和服务使用Twitter作为监测社区抑郁程度的数据源。拟议工作的意义在于三个方面。首先，我们的调查--既有实证的，也有理论的--将探索使用Twitter进行公共卫生监督的伦理问题。 这项工作有可能指导该领域未来的研究。其次，在开发和评估用于从推文中识别抑郁的算法和资源方面，我们正在为NLP领域做出基础工作。第三，开发这些算法和资源将为建立基于社交媒体的监测系统提供基础，这将提供一种具有成本效益的手段，以增强当前的精神健康监测实践。这一建议在其应用领域(微博以前从未被用于精神健康监测)、对使用NLP来识别公共卫生抑郁症状的关注以及定性生物伦理研究将在指导工作中发挥的核心作用方面都是创新的。

DESCRIPTION (provided by applicant): Major depressive disorder is one of the most common debilitating illnesses in the United States, with a lifetime prevalence of 16.2%. Currently, nationwide mental health surveillance takes the form of large-scale telephone- based surveys. These surveys have high running costs and require teams of human telephone operators. Even the largest system, the Behavioral Risk Factor Surveillance System, reaches only 0.13% of the US population. Twitter (and other microblog services) offers a rich, if terse, multilingual source of real time data for public health surveillance. Natural Language Processing (NLP) provides techniques and resources to "unlock" data from text. We propose using Twitter and NLP as a cost-effective and flexible approach to augmenting current telephone- based surveillance methods for population level depression monitoring. This grant application has two major strands. First, investigating ethical issues and challenges to privacy that emerge with the use of Twitter data for public health surveillance (Aim One). Second, developing techniques and resources for real-time public health surveillance for mental illness from Twitter (Aim Two &Aim Three). Aim One seeks to investigate and codify our responsibilities as researchers towards Twitter users by engaging with those users directly. With Aim Two, we will build and evaluate Natural Language Processing resources - algorithms, lexicons and taxonomies - to support the identification of depression symptoms in Twitter data. For Aim Three, we will build and evaluate Natural Language Processing modules and services that use Twitter as a data source for monitoring depression levels in the community. The significance of the proposed work lies in three areas. First, our investigations - both empirical and theoretical - will explore ethical issues in the use of Twitter for public health surveillance. This work has the potential to guide future research in the area. Second, in developing and evaluating algorithms and resources for identifying depression from tweets, we are contributing foundational work to the field of NLP. Third, developing these algorithms and resources will provide the bedrock for building social media based surveillance systems which will provide a cost effective means of augmenting current mental health surveillance practice. This proposal is innovative in both its application area (microblogs have not been used before for mental health surveillance), its focus on using NLP to identify depressive symptoms for public health, and in the central role that qualitative bioethical research will play in guiding the work.