Emergent Mind

Abstract

Illicit online pharmacies allow the purchase of prescription drugs online without a prescription. Such pharmacies leverage social media platforms such as Twit- ter as a promotion and marketing tool with the intent of reaching out to a larger, potentially younger demographics of the population. Given the serious negative health effects that arise from abusing such drugs, it is important to identify the relevant content on social media and exterminate their presence as quickly as pos- sible. In response, we collected all the tweets that contained the names of certain preselected controlled substances over a period of 5 months. We found that an unsupervised topic modeling based methodology is able to identify tweets that promote and market controlled substances with high precision. We also study the meta-data characteristics of such tweets and the users who post them and find that they have several distinguishing characteristics that sets them apart. We were able to train supervised methods and achieve high performance in detecting such content and the users who post them.

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