Mining Social Media Big Data for Toxicovigilance: Studying Substance Use via Natural Language Processing and Machine Learning Methods

美国的物质使用(SU)和物质使用障碍(SUD)的流行一直在演变为 几十年。多年来，处方药和非法药物都与过量死亡有关，值得注意的是 近年来合成阿片类药物(如芬太尼及其类似物)和精神刺激剂(如甲基苯丙胺)的增加 好几年了。高效新型精神活性物质(NPSS)的出现，如芬太尼类似物，具有 极大地促进了死亡人数的上升，并对治疗参与度和应对措施产生了不利影响。这个 COVID19大流行进一步加剧了这场危机，最近的研究也强调了 不同亚群之间在SUD治疗、研究、兴趣和反应方面存在差异， 种族/族裔少数群体受到不成比例的影响。解决危机的一个关键因素是改善 监视系统。具体地说，需要建立新的方法，以及时提供关于 由于传统的监测方法包括 有相当大的滞后。最近的许多研究发现，社交媒体(SM)是进行 SU/SUD监视。许多人使用SM来讨论个人经历、提供建议或寻求答案 关于SU/SUD的问题，导致了大量信息的产生。这样的信息可以 被定性、汇总和分析，以获得人口或亚人群层面的洞察，成本低， 近乎实时的。然而，将SM数据转换为及时的、可操作的知识并不是微不足道的，因为数据是 庞大、复杂和嘈杂，需要开发先进的、自动化的人工智能方法。 在国家药物滥用研究所的资助下，我们过去的工作主要集中在处方药上 (PM)，并建立了迄今为止最复杂的基于SM的数据挖掘管道。为回应这一事件 为应对南方传染病疫情的演变，拟议的项目将扩大我们的能力，以包括非法物质和 开发新的方法进行监测。具体地说，我们将(I)扩展我们的机器学习和自然 语言处理(NLP)分类管道可自动对Twitter上所有与SU相关的聊天内容进行分类 和Reddit(而不仅仅是PM)，(Ii)收集和分析队列自我报告的纵向时间表 SU/SUD，(3)根据年龄组、性别认同、种族等人口统计细节描述队列的特征 和地理定位，(Iv)开发先进的NLP驱动的方法来检测NPSS和SU/SUD的影响，(V) 研究疫情的短期和长期趋势和轨迹，(Vi)开展观察性研究 目标人群亚组，包括侧重于SU和SUD治疗差异和耻辱的研究，以及 (Vii)通过以下网络公开传播开发的方法和汇总的研究结果： 基于仪表板。我们以数据为中心的方法的实施和项目的成功执行 转变SU/SUD监控的潜力，并通过提供 接近实时统计和洞察，包括针对目标人群的统计和洞察。

The epidemic of substance use (SU) and substance use disorder (SUD) in the United States has been evolving for decades. Both prescription and illicit drugs have been involved in overdose deaths over the years, with notable increases in synthetic opioids (eg., fentanyl & analogs) and psychostimulants (eg., methamphetamine) in recent years. The emergence of high-potency novel psychoactive substances (NPSs), such as fentanyl analogs, have drastically contributed to rising deaths, and adversely impacted treatment engagement and response. The COVID19 pandemic has further exacerbated the crisis, and recent studies have also highlighted that substantial disparities exist in SUD treatment, research, interest, and response across different subpopulations, with racial/ethnic minorities being disproportionately impacted. A key element to tackling the crisis is improved surveillance. Specifically, there is a need for establishing novel approaches to provide timely insights about the trends, distributions, and trajectories of the SUD epidemic, as traditional surveillance approaches involve considerable lags. Many recent studies have identified social media (SM) as useful resources for conducting SU/SUD surveillance. Many people use SM to discuss personal experiences, provide advice, or seek answers to questions regarding SU/SUD, resulting in the generation of an abundance of information. Such information can be characterized, aggregated and analyzed to obtain population- or subpopulation-level insights, at low cost and in near real time. However, converting SM data into timely, actionable knowledge is non-trivial since the data is big, complex, and noisy, requiring the development of advanced, automated artificial intelligence methods. Funded by the National Institute on Drug Abuse, our past work focused specifically on prescription medications (PM) and established the most sophisticated SM-based data mining pipeline available to date. In response to the evolution of the SUD epidemic, the proposed project will extend our capabilities to include illicit substances and develop novel methods to conduct surveillance. Specifically, we will (i) extend our machine learning and natural language processing (NLP) classification pipeline to automatically classify all SU-related chatter from Twitter and Reddit (rather than PMs only), (ii) collect and analyze longitudinal timelines of cohorts self-reporting SU/SUD, (iii) characterize the cohorts in terms of demographic details such as age-group, gender identity, race and geolocation, (iv) develop advanced NLP-driven methods for detecting NPSs and impacts of SU/SUD, (v) study short-term and long-term trends and trajectories of the epidemic, (vi) conduct observational studies on targeted population subsets, including studies focusing on SU and SUD treatment disparities and stigma, and (vii) disseminate developed methodologies via open source code and aggregated findings publicly via a web- based dashboard. Implementation of our data-centric methods and successful execution of the project has the potential to transform SU/SUD surveillance, and complement traditional surveillance measures by providing close to real time statistics and insights, including those for targeted subpopulations.