Analyzing and retrieving illicit drug-related posts from social media
Tao Ding, Arpita Roy, Zhiyuan Chen, Qian Hua Zhu, Shimei Pan · 2016
Illicit drug use is a serious problem around the world. Social media has increasingly become an important tool for analyzing drug use patterns and monitoring emerging drug abuse trends. Accurately retrieving illicit drug-related social media posts is an important step in this research. Frequently, hashtags are used to identify and retrieve posts on a specific topic. However hashtags are highly ambiguous. Posts with the same hashtags are not always on the same topic. Moreover, hashtags are evolving, especially those related to illicit drugs. New street names are introduced constantly to avoid detection. In this paper, we employ topic modeling to disambiguate hashtags and track the changes of hashtags using semantic word embedding. Our preliminary evaluation shows the promise of these methods.