SABIA: An AI-Powered Tool for Detecting Opioid-Related Behaviors on Social Media

TL;DR

This paper introduces SABIA, a hybrid deep learning model that effectively detects opioid-related behaviors on social media, outperforming previous methods and aiding public health efforts.

Contribution

The study develops a novel BERT-BiLSTM-3CNN hybrid model, SABIA, tailored for classifying opioid-related behaviors in social media posts, with a new Reddit dataset and improved accuracy.

Findings

SABIA outperforms baseline models with 9.30% accuracy improvement.

The model effectively captures semantics and context in informal social media language.

SABIA achieves benchmark performance in opioid behavior classification.

Abstract

Social media platforms have become valuable tools for understanding public health challenges by offering insights into patient behaviors, medication use, and mental health issues. However, analyzing such data remains difficult due to the prevalence of informal language, slang, and coded communication, which can obscure the detection of opioid misuse. This study addresses the issue of opioid-related user behavior on social media, including informal expressions, slang terms, and misspelled or coded language. We analyzed the existing Bidirectional Encoder Representations from Transformers (BERT) technique and developed a BERT-BiLSTM-3CNN hybrid deep learning model, named SABIA, to create a single-task classifier that effectively captures the features of the target dataset. The SABIA model demonstrated strong capabilities in capturing semantics and contextual information. The proposed approach includes: (1) data preprocessing, (2) data representation using the SABIA model, (3) a fine-tuning phase, and (4) classification of user behavior into five categories. A new dataset was constructed from Reddit posts, identifying opioid user behaviors across five classes: Dealers, Active Opioid Users, Recovered Users, Prescription Users, and Non-Users, supported by detailed annotation guidelines. Experiments were conducted using supervised learning. Results show that SABIA achieved benchmark performance, outperforming the baseline (Logistic Regression, LR = 0.86) and improving accuracy by 9.30%. Comparisons with seven previous studies confirmed its effectiveness and robustness. This study demonstrates the potential of hybrid deep learning models for detecting complex opioid-related behaviors on social media, supporting public health monitoring and intervention efforts.