A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study

作者：Jiawei Li, Qing Xu, Neal Shah, Tim K. Mackey · 发表于：Journal of Medical Internet Research · 年份：2019 · DOI：10.2196/13803 · 被引用次数：96 · 研究领域：Opioid Use Disorder Treatment、Cybercrime and Law Enforcement Studies、Spam and Phishing Detection

BACKGROUND: Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug dealers selling controlled substances, opioids, and other illicit drugs. Previous studies and news investigations have reported the use of popular social media platforms as conduits for opioid sales. This study uses deep learning to detect illicit drug dealing on the image and video sharing platform Instagram. OBJECTIVE: The aim of this study was to develop and evaluate a machine learning approach to detect Instagram posts related to illegal internet drug dealing. METHODS: In this paper, we describe an approach to detect drug dealers by using a deep learning model on Instagram. We collected Instagram posts using a Web scraper between July 2018 and October 2018 and then compared our deep learning model against 3 different machine learning models (eg, random forest, decision tree, and support vector machine) to assess the performance and accuracy of the model. For our deep learning model, we used the long short-term memory unit in the recurrent neural network to learn the pattern of the text of drug dealing posts. We also manually annotated all posts collected to evaluate our model performance and to characterize drug selling conversations. RESULTS: From the 12,857 posts we collected, we detected 1228 drug dealer posts comprising 267 unique users. We used cross-validation to evaluate the 4 models, with our deep learning model reac...