Twitter-based detection of illegal online sale of prescription opioid

T.K. Mackey, J. Kalyanam, T. Katsuki and G. Lanckriet

American Journal of Public Health, 2017, vol. 107, issue 12, 1910-1915

Abstract: Objectives. To deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances. Methods. We first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015. Results. A total of 1778 tweets (

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2017.303994_3

DOI: 10.2105/AJPH.2017.303994

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