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

Jiaheng Xie, Zhu Zhang, Xiao Liu, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

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, 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.

Original languageEnglish (US)
Pages (from-to)166-195
Number of pages30
JournalJournal of Management Information Systems
Volume38
Issue number1
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • addiction treatment
  • Computational design science
  • deep learning
  • health IT
  • HealthTech
  • opioid addiction
  • social media analytics

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management

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