Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning

• 成果类型: Article
• 署名作者: Xie, Jiaheng; Zhang, Zhu; Liu, Xiao; Zeng, Daniel
• 署名单位: University of Delaware; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Arizona State University; Arizona State University-Tempe
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2021.1870388
• 发表日期: 2021
• 页码: 166-195

摘要

Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.