Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Xie, Jiaheng; Zhang, Zhu; Liu, Xiao; Zeng, Daniel
• Is Part Of: Journal of management information systems. Volume 38:Issue 1(2021)
• Page Start: 166
• Page End: 195
• Publication Date: 2021-01-02
• Subjects: Computational design science -- deep learning -- social media analytics -- health IT -- HealthTech -- opioid addiction -- addiction treatment; Management information systems -- Periodicals; Management information systems; Periodicals; 658.4038011
• DOI: 10.1080/07421222.2021.1870388
• Languages: English
• ISSNs: 0742-1222

Abstract

ABSTRACT: 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, SI milarity N etwork-based DE ep L earning (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.