Detecting Illicit Drug Usage on Twitter

Social media accounts for a large amount of the data produced on the Internet, informing and connecting many users across the world. Some social media, like Twitter, reveal valuable information about their users and user interests. In this project, we use social media to study opioid abuse, an emerging epidemic resulting in over 20,000 deaths in 2014 alone. We build tools to extract information about opioid use in the Rochester area and answer questions such as: in which regions of the metropolitan area do opioid abusers live? And, what leads them to opioid abuse? As a first step we use supervised and unsupervised learning to build classifiers for detecting mentions of opioid use and abuse in Twitter posts.