A Study of Social Web Data on Buprenorphine Abuse Using Semantic Web Technology

描述（由申请人提供）：阿片类药物的非医疗使用已被确定为美国增长最快的药物滥用形式之一，迫切需要通过提供额外和更及时的数据来加强当前的流行病学监测、早期预警和上市后监测系统。万维网已被确定为检测吸毒行为的模式和变化的“前沿”数据源之一。许多网站为个人提供了一个场所，让他们自由地分享自己的经验，提出问题，并对不同的药物发表评论。这种用户生成内容（UGC）可以作为一个非常丰富的数据源来研究与非法药物有关的知识、态度和行为。为了充分利用网络在药物滥用研究方面的潜力，该领域需要开发一种高度自动化的方式来访问、提取和分析与非法药物使用有关的基于网络的数据。这个探索性的R21是由多个主要研究者共同完成的，由赖特州立大学干预、治疗和成瘾研究中心（CITAR）和知识支持信息服务与科学中心（knoe .sis）的研究人员共同努力。本研究旨在利用语义网、自然语言处理和机器学习等前沿信息处理技术，对用户生成的内容进行定性和定量的内容分析，以达到以下目的：1)描述吸毒者对非法使用Suboxone(R)（丁丙诺啡/纳洛酮）和Subutex(R)（丁丙诺啡）相关的知识、态度和行为；2)识别和描述网络论坛反映的这些药物非法使用的时间模式。为了收集数据，这项研究将使用允许自由讨论非法药物、包含非法处方药使用信息并可供公众浏览的网站。这项研究将产生有关丁丙诺啡滥用做法的新信息，并将通过提供处理快速增长的网络数据所需的自动编码和信息提取工具，促进公共卫生和药物滥用研究的进步。本研究中应用的自动化信息提取方法将加强现有的早期预警和流行病学监测系统，并可在其他公共卫生领域推进定性和基于网络的研究方法。

DESCRIPTION (provided by applicant): The non-medical use of pharmaceutical opioids has been identified as one of the fastest growing forms of drug abuse in the U.S. There is a critical need to enhance current epidemiological monitoring, early warning, and post-marketing surveillance systems by providing additional and more timely data. The World Wide Web has been identified as one of the "leading edge" data sources for detecting patterns and changes in drug use practices. Many websites provide a venue for individuals to freely share their own experiences, post questions, and offer comments about different drugs. Such User Generated Content (UGC) can be used as a very rich data source to study knowledge, attitudes, and behaviors related to illicit drugs. To harness the full potential of the Web for drug abuse research, the field needs to develop a highly automated way of accessing, extracting, and analyzing Web-based data related to illicit drug use. This exploratory R21 is a multi-principal investigator, collaborative effort between researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) and the Center for Knowledge-Enabled Information Services and Science (Kno.e.sis) at Wright State University. The purpose of this Web-based study is to apply cutting-edge information processing techniques, such as the Semantic Web, Natural Language Processing, and Machine Learning, to qualitative and quantitative content analysis of user generated content to achieve the following aims: 1) Describe drug users' knowledge, attitudes, and behaviors related to the illicit use of Suboxone(R) (buprenorphine/naloxone) and Subutex(R) (buprenorphine); 2) Identify and describe temporal patterns of the illicit use of these drugs as reflected on web-based forums. To collect data, the study will use websites that allow for the free discussion of illicit drugs, contain information on illicit prescription drug use, and are accessible for public viewing. The study will generate new information about the practices of buprenorphine abuse and will contribute to the advancement of public health and substance abuse research by providing automatic coding and information extraction tools needed to handle rapidly growing Web-based data. Automated information extraction methods applied in this study will enhance current early warning and epidemiological surveillance systems and could advance qualitative and Web-based research methods in other areas of public health. PUBLIC HEALTH RELEVANCE: Building on inter-disciplinary collaboration and cutting-edge information processing techniques, this exploratory, Web-based study will generate new information about Suboxone(R) (buprenorphine/naloxone) and Subutex(R) (buprenorphine) abuse practices, thereby informing public health interventions and policy. It will also contribute to the advancement of public health and substance abuse research methods by providing automatic coding and information extraction tools needed to handle rapidly growing Web-based data.