Substance use, substance use disorder, and overdoses related to substance use are major public health problems globally and in the United States. A key aspect of addressing these problems from a public health standpoint is improved surveillance. Traditional surveillance systems are laggy, and social media are potentially useful sources of timely data. However, mining knowledge from social media is challenging, and requires the development of advanced artificial intelligence, specifically natural language processing (NLP) and machine learning methods. We developed a sophisticated end-to-end pipeline for mining information about nonmedical prescription medication use from social media, namely Twitter and Reddit. Our pipeline employs supervised machine learning and NLP for filtering out noise and characterizing the chatter. In this paper, we describe our end-to-end pipeline developed over four years. In addition to describing our data mining infrastructure, we discuss existing challenges in social media mining for toxicovigilance, and possible future research directions.

Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains an urgent need to develop novel strategies in the field of "digital epidemiology" to better identify, analyze and understand trends in NMUPD behavior. We conducted surveillance of the popular microblogging site Twitter by collecting 11 million tweets filtered for three commonly abused prescription opioid analgesic drugs Percocet (acetaminophen/oxycodone), OxyContin (oxycodone), and Oxycodone. Unsupervised machine learning was applied on the subset of tweets for each analgesic drug to discover underlying latent themes regarding risk behavior. A two-step process of obtaining themes, and filtering out unwanted tweets was carried out in three subsequent rounds of machine learning.