Lightweight and Effective Coded-Slang Detection for Cyber-Drug Intelligence

Drug-related criminal activities on social media increasingly employ dynamic coded language—such as fruit substitutions, numeric homophones, and dialectal metaphors—to evade detection. This linguistic obfuscation poses significant challenges to conventional keyword-based monitoring systems. Furthermore, the scarcity of open-source datasets capturing these specific evasive expressions severely impedes automated detection research. To address these limitations, we construct a dedicated dataset of 10000 samples of drug-related coded texts sourced from mainstream Chinese social media platforms. Concurrently, we propose an optimized, TextCNN-based deep learning framework tailored for the automated identification of such illicit content. By leveraging multi-scale convolutional feature extraction, our model effectively captures intricate local semantic patterns and morphological variations inherent in short, highly noisy social media texts. Experimental results demonstrate that the proposed method achieves an F1-score of 99.3%, significantly outperforming established baseline approaches in the semantic representation of coded language. These findings indicate that our framework provides an efficient, robust, and scalable computational solution for intelligent drug-related content monitoring in complex online environments.