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Social media may serve as an important platform for the monitoring of population-level opioid abuse in near real-time. Our objectives for this study were to (i) manually characterize a sample of opioid-mentioning Twitter posts, (ii) compare the rates of abuse/misuse related posts between prescription and illicit opiods, and (iii) to implement and evaluate the performances ofsupervised machine learning algorithms for the characterization of opioid-related chatter, which can potentially automatedoi:10.3233/shti190238 pmid:31437940 pmcid:PMC6774610 fatcat:gqz7ujnk4bcidjli4hfsi7tmoq