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According to NSDUH (National Survey on Drug Use and Health), 20 millionAmericans consumed drugs in the past few 30 days. Combating illicit drug use isof great interest to public health and law enforcement agencies. Despite of theimportance, most of the existing studies on drug uses rely on surveys. Surveyson sensitive topics such as drug use may not be answered truthfully by thepeople taking them. Selecting a representative sample to survey is anothermajor challenge. In this paper, we explore the possibility of using bigmultimedia data, including both images and text, from social media in order todiscover drug use patterns at fine granularity with respect to demographics.Instagram posts are searched and collected by drug related terms by analyzingthe hashtags supplied with each post. A large and dynamic dictionary offrequent drug related slangs is used to find these posts. User demographics areextracted using robust face image analysis algorithms. These posts are thenmined to find common trends with regard to the time and location they areposted, and further in terms of age and gender of the drug users. Furthermore,by studying the accounts followed by the users of drug related posts, weextract common interests shared by drug users.

Fine-grained Mining of Illicit Drug Use Patterns Using Social Multimedia Data from Instagram