Auditing Social Media Algorithmic Pathways to Measure Prevalence of Online Misinformation Related to Opioid Misuse

审核社交媒体算法路径以衡量与阿片类药物滥用相关的在线错误信息的流行程度

• 批准号: 10666308
• 负责人: Tanushree Mitra
• 金额: $ 24.42万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666308
• 关键词: Address; Affect; Age; Algorithm Design; Algorithms; Automobile Driving; Awareness; Behavior; Behavior Disorders; Behavior Therapy; COVID-19 pandemic; Cause of Death; Characteristics; Clinical; Clinical Treatment; Computers; Counseling; Coupled; Dangerousness; Data; Diagnosis; Early Diagnosis; Environment; Epidemic; Evaluation; Evidence based intervention; Exposure to; Facebook; Foundations; Future; Gender; Goals; Health; Improve Access; Individual; Infrastructure; Intelligence; Internet; Intervention; Interview; Investigation; Knowledge; Letters; Light; Location; Machine Learning; Measures; Methodology; Methods; Misinformation; Morphologic artifacts; National Institute of Drug Abuse; Natural Language Processing; Outcome; Participant; Pathway interactions; Patient-Focused Outcomes; Patients; Persons; Pharmaceutical Preparations; Policies; Prevalence; Psychologist; Public Health; Qualitative Evaluations; Reaction; Recommendation; Recovery; Rehabilitation therapy; Research; Research Design; Resources; Risk; Risk Behaviors; Risk Reduction; Role; Scientist; Site; Sorting; Statistical Data Interpretation; Strategic Planning; Substance Use Disorder; Surface; Symbiosis; System; Techniques; Technology; Time; Treatment outcome; Twitter; United States; Work; addiction; behavioral health; cost effective; design; detection method; disorder risk; empowerment; experimental study; health literacy; improved; innovation; insight; interest; machine learning method; medication for opioid use disorder; medication-assisted treatment; multidisciplinary; new technology; novel; online resource; opioid misuse; opioid overdose; opioid use disorder; overdose risk; prevent; recruit; response; sharing platform; social media; social stigma; standard care; substance misuse; sustained recovery; tool

Abstract: Opioid misuse has become a public health epidemic in the United States with more than 70% of indi- viduals with an opioid use disorder (OUD) never receiving any sort of treatment. Even fewer receive medications for addiction treatment (MAT)—the gold standard for treatment and a safe, cost-effective way to reduce the risk of overdose while improving the likelihood of sustained recovery. Due to the stigma surrounding opioid misuse, in- dividuals often seek non-conventional ways to recover, such as using online resources, specifically social media, and in particular microblogging sites like Twitter. However, social media platforms are often rife with MAT misin- formation (MATM), posing a serious barrier to recovery. Moreover, the harmful effects of online misinformation are further exacerbated by the design of the algorithms that drive content curation or recommendation on social media sites. Yet, research on understanding algorithmic pathways to health-misinformation is rare and that re- lated to opioid misuse is practically non-existent. This R21 proposal will address this gap by conducting formative research through the use of robust audit methodologies coupled with rigorously validated machine learning (ML) techniques, to lay bare an unexplored phenomena in the OUD medication and treatment domain—algorithmically curated MATM in online social media systems, specifically Twitter—one of the most widely used social media platforms for sharing and seeking OUD information. The work advances this research agenda by leveraging the team’s pioneering research in addressing two of the key technical challenges driving this proposal: a) building computational approaches to audit black-box platform algorithms that curate, recommend, or filter information viewed by end users; and 2) developing ML techniques that detect pre-existing or emergent online misinforma- tion. Drawing from advances in algorithmic audit work and PI’s own successful audit study designs, Aim 1 will build tools and methodologies to audit search and recommendation algorithms for MATM on Twitter across vari- ous individual user characteristics and algorithmic inputs. The developed methodologies will be generic enough to be adaptable across other social media platforms. In Aim 2, we will leverage these methodologies to conduct an exhaustive set of carefully controlled audit experiments on Twitter to investigate it’s search and recommenda- tion algorithms’ tendency to surface MATM. We will also develop and evaluate ML methods that can automatically determine whether the collected social media posts contain MATM. Finally, in Aim 3 we will develop a mixed- methods approach to quantitatively and qualitatively validate our audit results with participants on Twitter who misuse opioids. The project brings together a multidisciplinary team of computer scientists and a clinical psychol- ogist, with expertise in social media analytics and recruitment, online algorithmic audits, substance use disorders, machine learning, and natural language processing. The knowledge we produce will set the stage for future re- search in early detection of risky OUD behaviors, understanding the role of the online information environment in exacerbating or preventing OUD risks and launching evidence-based interventions to mitigate such risks.

摘要：阿片类药物滥用已成为美国的公共卫生流行病，超过70％ 阿片类药物使用障碍（OUD）的视频从未接受任何治疗。甚至更少收到药物 对于成瘾治疗（MAT） - 治疗的黄金标准以及一种安全，具有成本效益的方式来降低 过量的同时改善了持续恢复的可能性。由于围绕阿片类药物滥用的污名， 分裂通常寻求恢复的非规定方法，例如使用在线资源，特别是社交媒体， 特别是Twitter等微博网站。但是，社交媒体平台通常会被垫子误解 - 组（MATM），构成了严重的恢复障碍。此外，在线失误的有害影响 通过推动内容策划或建议社交建议的算法的设计进一步加剧了 媒体网站。然而，关于理解健康释放算法途径的研究很少 对阿片类药物的遗留实际上是不存在的。该R21提案将通过进行格式来解决此差距 通过使用强大的审核方法以及严格验证的机器学习（ML）的研究 技术，在OUD药物和治疗领域中裸露出意外的现象 - 算术上 在线社交媒体系统（特别是Twitter）中策划的MATM是最广泛使用的社交媒体之一 共享和寻求Oud信息的平台。这项工作通过利用 团队开创性的研究解决了推动此建议的两个主要技术挑战：a）建造 审核黑框平台算法的计算方法，该算法策划，建议或过滤信息 由最终用户查看； 2）开发ML技术，以检测现有或新兴的在线Misineminforma- tion。从算法审计工作和PI自己成功的审计研究设计中的进步中获胜，AIM 1 Will 构建工具和方法，以审核搜索和推荐在Twitter上的MATM的算法。 OUS个人用户特征和算法输入。开发的方法将足够通用 在其他社交媒体平台上具有适应性。在AIM 2中，我们将利用这些方法来进行 在Twitter上进行了一套详尽的精心控制的审计实验，以调查其搜索和建议 - Tion算法表面MTM的趋势。我们还将开发和评估可以自动的ML方法 确定收集到的社交媒体帖子是否包含MATM。最后，在AIM 3中，我们将开发一个混合 在Twitter上与参与者进行定量和定性验证我们的审计结果的方法方法 滥用阿片类药物。该项目汇集了一个计算机科学家的多学科团队和一个临床心理学 Ogist，具有社交媒体分析和招聘方面的专业知识，在线算法审核，药物使用障碍， 机器学习和自然语言处理。我们产生的知识将为将来的重新奠定舞台 在早期发现风险的OUD行为时搜索，了解在线信息环境中的作用 加剧或防止OUD风险并发起基于证据的干预措施以减轻此类风险。