title: Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning.
abstract: 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.
  
  
  
  Using this methodology, 2.3M tweets were identified that contained content relevant to analgesic NMUPD. The underlying themes were identified for each drug and the most representative tweets of each theme were annotated for NMUPD behavioral risk factors. The primary themes identified evidence high levels of social media discussion about polydrug abuse on Twitter. This included specific mention of various polydrug combinations including use of other classes of prescription drugs, and illicit drug abuse.
  
  
  
  This study presents a methodology to filter Twitter content for NMUPD behavior, while also identifying underlying themes with minimal human intervention. Results from the study track accurately with the inclusion/exclusion criteria used to isolate NMUPD-related risk behaviors of interest and also provides insight on NMUPD behavior that has a high level of social media engagement. Results suggest that this could be a viable methodology for use in big data substance abuse surveillance, data collection, and analysis in comparison to other studies that rely upon content analysis and human coding schemes.
authors: Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, USA.
authors: Janani
authors: J
authors: Kalyanam
authors: Kavli Institute for Brain and Mind, University of California, San Diego, San Diego, CA, USA.
authors: Takeo
authors: T
authors: Katsuki
authors: Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, USA.
authors: Gert
authors: G
authors: R G Lanckriet
authors: Department of Anesthesiology, University of California, San Diego, School of Medicine, San Diego, CA, USA; Division of Global Public Health, University of California, San Diego, School of Medicine, Department of Medicine, San Diego, CA, USA; Global Health Policy Institute, San Diego, CA, USA. Electronic address: tmackey@ucsd.edu.
authors: Tim K
authors: TK
authors: Mackey
mesh_terms: D006801:Humans; D009293:Opioid-Related Disorders / Q000453:epidemiology*; D063487:Prescription Drug Misuse / Q000706:statistics & numerical data*; D012307:Risk Factors; D061108:Social Media / Q000706:statistics & numerical data*; D000069558:Unsupervised Machine Learning / Q000706:statistics & numerical data*
keywords: Digital surveillance; Nonmedical use of prescription drugs; Prescription opioid abuse; Social media; Substance abuse; Twitter