  Illicit drug trafficking via social media sites such as Instagram has become
a severe problem, thus drawing a great deal of attention from law enforcement
and public health agencies. How to identify illicit drug dealers from social
media data has remained a technical challenge due to the following reasons. On
the one hand, the available data are limited because of privacy concerns with
crawling social media sites; on the other hand, the diversity of drug dealing
patterns makes it difficult to reliably distinguish drug dealers from common
drug users. Unlike existing methods that focus on posting-based detection, we
propose to tackle the problem of illicit drug dealer identification by
constructing a large-scale multimodal dataset named Identifying Drug Dealers on
Instagram (IDDIG). Totally nearly 4,000 user accounts, of which over 1,400 are
drug dealers, have been collected from Instagram with multiple data sources
including post comments, post images, homepage bio, and homepage images. We
then design a quadruple-based multimodal fusion method to combine the multiple
data sources associated with each user account for drug dealer identification.
Experimental results on the constructed IDDIG dataset demonstrate the
effectiveness of the proposed method in identifying drug dealers (almost 95%
accuracy). Moreover, we have developed a hashtag-based community detection
technique for discovering evolving patterns, especially those related to
geography and drug types.
