Fine-grained Mining of Illicit Drug Use Patterns Using Social Multimedia Data from Instagram

TL;DR

This study leverages social multimedia data from Instagram, including images and text, to identify and analyze fine-grained patterns of illicit drug use across demographics, offering an alternative to traditional survey methods.

Contribution

It introduces a novel multimedia data mining approach from social media to uncover detailed drug use patterns, overcoming survey limitations.

Findings

Identified drug use trends based on hashtags and slang.

Extracted demographic information using face analysis.

Discovered shared interests among drug users.

Abstract

According to NSDUH (National Survey on Drug Use and Health), 20 million Americans consumed drugs in the past few 30 days. Combating illicit drug use is of great interest to public health and law enforcement agencies. Despite of the importance, most of the existing studies on drug uses rely on surveys. Surveys on sensitive topics such as drug use may not be answered truthfully by the people taking them. Selecting a representative sample to survey is another major challenge. In this paper, we explore the possibility of using big multimedia data, including both images and text, from social media in order to discover drug use patterns at fine granularity with respect to demographics. Instagram posts are searched and collected by drug related terms by analyzing the hashtags supplied with each post. A large and dynamic dictionary of frequent drug related slangs is used to find these posts. User demographics are extracted using robust face image analysis algorithms. These posts are then mined to find common trends with regard to the time and location they are posted, and further in terms of age and gender of the drug users. Furthermore, by studying the accounts followed by the users of drug related posts, we extract common interests shared by drug users.