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.

翻译后摘要：阿片类药物滥用已成为一种公共卫生流行病，在美国超过70%的indi- 患有阿片类药物使用障碍（OUD）的患者从未接受过任何治疗。接受药物治疗的人更少 成瘾治疗（MAT）-治疗的黄金标准和安全，具有成本效益的方法，以减少 同时提高持续康复的可能性。由于阿片类药物滥用的耻辱，在- 企业通常寻求非传统的方式来恢复，例如使用在线资源，特别是社交媒体， 特别是像Twitter这样的微博网站。然而，社交媒体平台往往充斥着MAT misin- 形成（MATM），对恢复构成严重障碍。此外，网上错误信息的有害影响 在社交媒体上推动内容策展或推荐的算法设计进一步加剧了这一问题。 媒体网站。然而，关于理解健康错误信息的算法途径的研究很少， 与阿片类药物滥用有关的情况几乎不存在。这项R21提案将通过开展形成性的 通过使用强大的审计方法以及经过严格验证的机器学习（ML）进行研究 技术，揭示了OUD药物和治疗领域的未探索现象-算法 在线社交媒体系统中的策划MATM，特别是Twitter-最广泛使用的社交媒体之一 提供共享和寻求OUD信息的平台。这项工作通过利用 团队的开拓性研究，在解决两个关键的技术挑战，推动这一建议：a）建设 审计黑盒平台算法的计算方法，这些算法用于管理、推荐或过滤信息 由最终用户查看; 2）开发ML技术，检测预先存在的或新出现的在线错误信息， 是的。借鉴算法审计工作的进展和PI自己成功的审计研究设计，目标1将 建立工具和方法，以审计搜索和推荐算法的MATM在Twitter上跨瓦里 我们的个人用户特征和算法输入。制定的方法将足够通用 能够适应其他社交媒体平台。在目标2中，我们将利用这些方法进行 在Twitter上进行了一系列严格控制的审计实验，以调查它的搜索和广告- 算法的表面MATM的趋势。我们还将开发和评估ML方法， 确定所收集的社交媒体帖子是否包含MATM。最后，在目标3中，我们将开发一个混合- 方法的方法来定量和定性地验证我们的审计结果与Twitter上的参与者， 滥用阿片类药物该项目汇集了计算机科学家和临床心理学的多学科团队， ogist，在社交媒体分析和招聘，在线算法审计，物质使用障碍， 机器学习和自然语言处理。我们创造的知识将为未来的重建奠定基础。 搜索在早期发现危险的OUD行为，了解在线信息环境的作用， 加剧或预防OUD风险，并启动基于证据的干预措施以减轻此类风险。