An unsupervised machine learning approach for the detection and characterization of illicit drug-dealing comments and interactions on Instagram. Issue 1 (1st December 2022)

• Record Type: Journal Article
• Authors: Shah, Neal; Li, Jiawei; Mackey, Tim K.
• Is Part Of: Substance abuse. Volume 43:Issue 1(2022)
• Page Start: 273
• Page End: 277
• Publication Date: 2022-12-01
• Subjects: Online drug marketplace -- social media -- Instagram -- opioids -- opioid use disorder -- infoveillance; Substance abuse -- Periodicals; Medical education -- Periodicals; Education, Medical -- periodicals; Substance Abuse -- periodicals; 362.29
• DOI: 10.1080/08897077.2021.1941508
• Languages: English
• ISSNs: 0889-7077

Abstract

Abstract: Background: Growing use of social media has led to the emergence of virtual controlled substance and illicit drug marketplaces, prompting calls for action by government and law enforcement. Previous studies have analyzed Instagram drug selling via posts. However, comments made by users involving potential drug selling have not been analyzed. In this study, we use unsupervised machine learning to detect and classify prescription and illicit drug-related buying and selling interactions on Instagram. Methods: We used over 1, 000 drug-related hashtags on Instagram to collect a total of 43, 607 Instagram comments between February 1st, 2019 and May 31st, 2019 using data mining approaches in the Python programming language. We then used an unsupervised machine learning approach, the Biterm Topic Model (BTM), to thematically summarize Instagram comments into distinct topic groupings, which were then extracted and manually annotated to detect buying and selling comments. Results: We detected 5, 589 comments from sellers, prospective buyers, and online pharmacies from 531 unique posts. The vast majority (99.7%) of comments originated from drug sellers and online pharmacies. Key themes from comments included providing contact information through encrypted third-party messaging platforms, drug availability, and price inquiry. Commonly offered drugs for sale included scheduled controlled substances such as Adderall and Xanax, as well as illicit hallucinogens and stimulants. Comments from prospective buyers of drugs most commonly included inquiries about price and availability. Conclusions: We detected prescription controlled substances and other illicit drug selling interactions via Instagram comments to posts. We observed that comments were primarily used by sellers offering drugs, and typically not by prospective buyers interacting with sellers. Further research is needed to characterize these "social" drug marketplace interactions on this and other popular social media platforms.