Drug Abuse Detection in Twitter-sphere: Graph-Based Approach

Abstract

The rate of non-medical use of opioid drugs has increased markedly since the early 2000s. Due to this non-medical use, abusers suffer from different adverse effects that include physical and psychological problems. Many studies have been done to detect Drug Abuse (DA) events from social media data using machine learning and deep learning concepts. Moreover, Graph Neural Networks (GNNs) have recently become popular in text classification tasks due to their high accuracy and capability to handle complex structures. In this work, we collect drugs-related Twitter data (tweets) and build text graphs (corpus-level and document-level) to capture word-word, document-word, and document-document relations. Then we apply different GNN models on those text graphs and thus turn the text classification task into a node classification (for corpus-level graph) and graph classification (for document-level graph) task to detect DA events. Finally, we compare our graph-based DA detection models with different types of baselines models, including rule-based, traditional machine learning, and deep learning models. Our result shows graph-based models outperform the traditional machine learning and deep learning-based models.