Opioid addiction is one of the largest and deadliest epidemics in the United States. This research investigates opioids’ epidemic by analyzing recent tweets data for users who are addicted or have been addicted to opioids. Automatically analyzing social media users’ posts of opioids addicted users using machine learning tools can help understand the themes and topics that exist in the up-to-date discussions of online users of social media networks. Through the analysis period from 01/01/2015 to 02/25/2019, we were able to identify 571 self-identified Twitter users. We collected a total of 20,609 English-language tweets that belong to the self-identified users. Overall, we identify the different recovery approaches, illicit drug use and user seeking for help. This study helps elicit how the daily posts of online social media users can provide a better understanding of the opioid crisis, and strengthen the public health data reporting and collection for opioids epidemic.
Nasralah, T., El-Gayar, O., & Wang, Y. (2019, July 12). What Social Media Can Tell Us About Opioid Addicts: Twitter Data Case Analysis. Proceedings of the 25th Americas Conference on Information Systems (AMCIS ’19).