Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach

Abstract

Opioid use disorder (OUD) costs the US healthcare systems $504 billion annually and poses significant mortality risk. Current studies attempt to understand barriers to OUD treatment using surveys to mitigate this opioid crisis. However, the response rate of these surveys is low because of social stigma. 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 barriers to OUD treatment 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%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for social media analytics. We also provide a deeper understanding of the opioid epidemic and the hurdles that patients face.