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

Jiawei Li,Qing Xu,Neal Shah,Tim K Mackey

doi:10.2196/13803

Abstract

BackgroundSocial 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.ObjectiveThe aim of this study was to develop and evaluate a machine learning approach to detect Instagram posts related to illegal internet drug dealing.MethodsIn 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.ResultsFrom 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 reaching 95% on F1 score and performing better than the other 3 models. We also found that by removing the hashtags in the text, the model had better performance. Detected posts contained hashtags related to several drugs, including the controlled substance Xanax (1078/1228, 87.78%), oxycodone/OxyContin (321/1228, 26.14%), and illicit drugs lysergic acid diethylamide (213/1228, 17.34%) and 3,4-methylenedioxy-methamphetamine (94/1228, 7.65%). We also observed the use of communication applications for suspected drug trading through user comments.ConclusionsOur approach using a combination of Web scraping and deep learning was able to detect illegal online drug sellers on Instagram, with high accuracy. Despite increased scrutiny by regulators and policymakers, the Instagram platform continues to host posts from drug dealers, in violation of federal law. Further action needs to be taken to ensure the safety of social media communities and help put an end to this illicit digital channel of sourcing.

Highlights

BackgroundIn June 2018, the US Food and Drug Administration (FDA) held the Online Opioid Summit, a 1-day meeting seeking to generate momentum around the need to combat illicit internet sales of opioids [1]
Our approach using a combination of Web scraping and deep learning was able to detect illegal online drug sellers on Instagram, with high accuracy
The study analyzed the user timelines of identified posts to differentiate drug dealers from users who exhibited drug use behavior and achieved a high classification accuracy of 88% [17]. Building on these prior studies that have used different big data and machine learning approaches to detect substance abuse behavior and illegal drug selling on social media, this study describes the use and evaluation of a deep learning model to better automate the detection of illegal opioid and other illicit drug sales on Instagram

Summary

Introduction

BackgroundIn June 2018, the US Food and Drug Administration (FDA) held the Online Opioid Summit, a 1-day meeting seeking to generate momentum around the need to combat illicit internet sales of opioids [1]. In addition to federal agencies, several internet and social media companies were in attendance, including Google (which operates YouTube and Google+), Twitter, Facebook (which operates Instagram and WhatsApp), Pinterest, and other e-commerce, technology, and patient safety organizations [2]. Federal law explicitly prohibits the internet sale of controlled substances as enforced by the 2008 Ryan Haight Online Pharmacy Consumer Protection Act (RHA) [4,5]. Named after a Californian adolescent who died in 2001 after overdosing on Vicodin purchased from an online drug seller without a prescription, the RHA was meant to curb the use of the internet as an alternative and convenient channel of sourcing [6]. Previous studies and news investigations have reported the use of popular social media platforms as conduits for opioid sales.

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