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%的印度人滥用阿片类药物