Understanding Illicit Drug Use Behaviors by Mining Social Media

Zhou, Yiheng; Sani, Numair; Lee, Chia Kuei; Luo, Jiebo

doi:10.48550/arxiv.1604.07096

Cited by 5 publications

(2 citation statements)

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“…Scott et al, 2015), and adverse drug reaction (Lardon et al, 2015;Sarker et al, 2015). One of the most similar studies to the current study was found in Zhou, et al (2016). They used social media mining by analyzing the hashtags supplied with each Instagram post to understand illicit drug use behaviors.…”

Section: Social Media Miningmentioning

confidence: 56%

A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work

Wang

Cheung

Leung

et al. 2017

International Journal of Knowledge and Systems Science

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Cyber youth work is a pioneering and proactive approach used by non-government organizations to address the changing needs of the youth groups, particularly those at-risk and “hidden” young people. This paper describes the development process of a social media mining and analysis method, which is built to facilitate services for cyber youth work. The method incorporates an iterative method for collecting, selecting and extracting domain keywords in selected social media. A hybrid approach which combines heuristic rules and n-gram analysis for bilingual word segmentation has been developed. Then, social network analysis is used to analyze the extracted results. A pilot study has been done by using drug abuse as the study topic. It demonstrated high potential of the method to enhance cyber youth work. The weights deduced by the method have found to have a positive correlation with the benchmarked scores. It helps to understand the characteristic of the network, identify target clients, and provide data support for marking decisions.

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Section: Social Media Miningmentioning

confidence: 56%

A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work

Wang

Cheung

Leung

et al. 2017

International Journal of Knowledge and Systems Science

View full text Add to dashboard Cite

show abstract

“…As far as we know, there has been limited work on tracking drug abuse and illicit drug trade from online data. Among these existing works, [5] analyzed the time and location patterns of drug use by mining Twitter data; network information of Instagram user timelines was used in [8] to monitor suspicious drug interaction activities; [85] and [76] analyzed Instagram data for tracking and identifying drug dealer accounts. More recently, machine learning and natural language processing techniques have been applied to combat prescription drug abuse [39,63,64] and detect illicit drug dealers [44,81].…”

Section: Drug Abuse and Dealing Analysismentioning

confidence: 99%

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

Hu¹,

Yin²,

Liu³

et al. 2021

Preprint

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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.

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Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use

Dou

Barman‐Adhikari

Fang

et al. 2021

AAAI

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Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on self-reports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect ~135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of ~0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of ~0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.

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Understanding Illicit Drug Use Behaviors by Mining Social Media

Cited by 5 publications

References 0 publications

A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work

A Social Media Mining and Analysis Approach for Supporting Cyber Youth Work

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

Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use

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