Utilizing social media as a resource for mental health surveillance

利用社交媒体作为心理健康监测的资源

• 批准号: 8911360
• 负责人: Michael Ambrose Conway
• 金额: $ 21.74万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8911360
• 关键词: Algorithms; Applications Grants; Area; Attitude; Behavioral Risk Factor Surveillance System; Broadcast Media; Cities; Cognitive; Communities; County; Data; Data Sources; Dental; Development; Disasters; Earthquakes; Electronic Health Record; Epidemiology; Ethical Issues; Ethics; Exercise; Guidelines; Health; Human; Influenza A Virus, H1N1 Subtype; Interview; Investigation; Linguistics; Location; Major Depressive Disorder; Mental Depression; Mental Health; Mental disorders; Methods; Mining; Monitor; Natural Language Processing; Participant; Phase; Play; Population; Population Surveillance; Prevalence; Privacy; Process; Psyche structure; Public Health; Recruitment Activity; Reporting; Research; Research Personnel; Resources; Role; Running; Scheme; Services; Smoking Status; Source; Surveillance Methods; Surveys; System; Taxonomy; Techniques; Telephone; Text; Time; United States; Update; Work; base; center for epidemiological studies depression scale; cost; cost effective; depressive symptoms; flexibility; innovation; lexical; social; syndromic surveillance; text searching; tool; ward

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.

描述（由申请人提供）：重度抑郁症是美国最常见的衰弱性疾病之一，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，也只覆盖了美国人口的0.13%。推特（和其他微博服务）为公共卫生监督提供了一个丰富的、简洁的、多语种的真实的时间数据源。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作为一种具有成本效益和灵活性的方法，以增强当前基于电话的人群水平抑郁症监测方法。 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（Aim One）时出现的伦理问题和隐私挑战。第二，开发技术和资源，从Twitter上实时监测精神疾病的公共卫生（目标二和目标三）。Aim One旨在通过直接与Twitter用户接触，调查和编纂我们作为研究人员对Twitter用户的责任。通过目标二，我们将构建和评估自然语言处理资源-算法，词汇和分类-以支持Twitter数据中抑郁症状的识别。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作为监测社区抑郁水平的数据源。拟议工作的意义在于三个方面。首先，我们的调查-经验和理论-将探讨使用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的未来研究。其次，在开发和评估从推文中识别抑郁症的算法和资源时，我们正在为NLP领域做出基础性工作。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，这将为增强当前的心理健康监测实践提供一种具有成本效益的手段。这一建议在其应用领域（微博以前没有被用于精神健康监测），其重点是使用NLP来识别公共卫生的抑郁症状，以及定性生物伦理学研究在指导工作中发挥的核心作用方面都是创新的。