Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion
نویسندگان
چکیده
Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing great deal of attention from law enforcement and public health agencies. How to identify illicit dealers data has remained technical challenge for the following reasons. On one hand, available are limited because privacy concerns with crawling sites; on other diversity dealing patterns makes it difficult reliably distinguish common users. Unlike existing methods that focus posting-based detection, we propose tackle problem dealer identification by constructing large-scale multimodal dataset named Identifying Drug Dealers (IDDIG). Nearly 4,000 user accounts, which more than 1,400 dealers, been collected multiple sources including post comments, images, homepage bio, images. We then design quadruple-based fusion method combine associated each account identification. Experimental results constructed IDDIG demonstrate effectiveness proposed in identifying (almost 95% accuracy). Moreover, developed hashtag-based community detection technique discovering evolving patterns, especially those related geography types.
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2021
ISSN: ['2157-6904', '2157-6912']
DOI: https://doi.org/10.1145/3472713