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一种公共卫生流行病，超过 70% 的人滥用阿片类药物。 患有阿片类药物使用障碍 (OUD) 的人从未接受过任何形式的治疗。接受药物治疗的人更少 成瘾治疗 (MAT)——治疗的黄金标准，也是降低成瘾风险的安全、经济有效的方法 服用过量，同时提高持续康复的可能性。由于阿片类药物滥用带来的耻辱， 个人经常寻求非传统的恢复方式，例如使用在线资源，特别是社交媒体， 特别是 Twitter 等微博网站。然而，社交媒体平台常常充斥着 MAT 误解。 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 推动社交内容管理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路径的研究很少，并且重新 与阿片类药物滥用相关的情况实际上不存在。 R21 提案将通过开展形成性工作来解决这一差距 通过使用稳健的审计方法和经过严格验证的机器学习 (ML) 进行研究 技术，以算法的方式揭示 OUD 药物和治疗领域中未经探索的现象 在在线社交媒体系统中策划 MATM，特别是 Twitter——使用最广泛的社交媒体之一 共享和寻找 OUD 信息的平台。这项工作通过利用 团队的开创性研究解决了推动该提案的两个关键技术挑战：a）构建 审计黑盒平台算法的计算方法，这些算法可以策划、推荐或过滤信息 最终用户查看； 2）开发机器学习技术来检测预先存在或新出现的在线错误信息 。借鉴算法审计工作的进步和 PI 自己成功的审计研究设计，目标 1 将 构建工具和方法来审核 Twitter 上 MATM 的跨变量搜索和推荐算法 我们的个人用户特征和算法输入。开发的方法将足够通用 适应其他社交媒体平台。在目标 2 中，我们将利用这些方法来进行 在 Twitter 上进行了一系列详尽的仔细控制的审计实验，以调查其搜索和推荐 化算法呈现 MATM 的趋势。我们还将开发和评估能够自动 确定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- 方法与 Twitter 上的参与者一起定量和定性地验证我们的审计结果 滥用阿片类药物。该项目汇集了一个由计算机科学家和临床心理学组成的多学科团队 ogist 拥有社交媒体分析和招聘、在线算法审计、药物滥用障碍方面的专业知识， 机器学习和自然语言处理。我们产生的知识将为未来的再创造奠定基础 早期发现有风险的 OUD 行为的搜索，了解在线信息环境在 加剧或预防 OUD 风险，并采取基于证据的干预措施来减轻此类风险。