An Innovative Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Project Finalist at HHS Opioid Code-a-Thon

An Innovative Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Project Finalist at HHS Opioid Code-a-Thon

JMIR Preprints, February 2018

29613851

Tim Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi Gupta

On December 6-7, 2017, the U.S. Department of Health and Human Services hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. Authors, comprised of an interdisciplinary team from academia, the private sector, and the U.S. Centers for Disease Control and Prevention, participated in the Code-a-Thon as part of the "Prevention" track. To develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Tweets were collected from the Twitter public API stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15 - December 5, 2017. An unsupervised machine learning-based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the Tweets to isolate those clusters associated with illegal online marketing and sale using a Biterm Topic Model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess characteristics of illegal online sellers. We collected and analyzed 213K tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl and hydrocodone. Using BTM, 692 (0.3%) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique "live" tweets with 15 (44.1%) directing consumers to illicit online pharmacies, 11 (32.4%) linked to individual drug sellers, and 7 (20.6%) used by marketing affiliates. In addition to offering the "no prescription" sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Results of this study confirm prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the U.S. Food and Drug Administration and U.S. Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public. not applicable.