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

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion