Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning

•A methodology using unsupervised machine learning analyzed 11 million tweets filtered for commonly abused prescription opioid drugs•Analyses identified 2.3 million tweets with content relevant to nonmedical use of prescription medications/drugs (NMUPD)•Twitter content was associated with a high degree of discussion (approximately 80%) about polydrug abuse involving multiple types of substances•The methodology can filter large volumes of twitter data with minimal human intervention to identify macro NMUPD themes and trends