Identifying high-impact opioid products and key sellers in dark net marketplaces: An interpretable text analytics approach

Po Yi Du, Mohammadreza Ebrahimi, Ning Zhang, Hsinchun Chen, Randall A. Brown, Sagar Samtani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the Internet based applications become more and more ubiquitous, drug retailing on Dark Net Marketplaces (DNMs) has raised public health and law enforcement concerns due to its highly accessible and anonymous nature. To combat illegal drug transaction among DNMs, authorities often require agents to impersonate DNM customers in order to identify key actors within the community. This process can be costly in time and resource. Research in DNMs have been conducted to provide better understanding of DNM characteristics and drug sellers' behavior. Built upon the existing work, researchers can further leverage predictive analytics techniques to take proactive measures and reduce the associated costs. To this end, we propose a systematic analytical approach to identify key opioid sellers in DNMs. Utilizing machine learning and text analysis, this research provides prediction of high-impact opioid products in two major DNMs. Through linking the high-impact products and their sellers, we then identify the key opioid sellers among the communities. This work intends to help law enforcement authorities to formulate strategies by providing specific targets within the DNMs and reduce the time and resources required for prosecuting and eliminating the criminals from the market.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
EditorsXiaolong Zheng, Ahmed Abbasi, Michael Chau, Alan Wang, Lina Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages110-115
Number of pages6
ISBN (Electronic)9781728125046
DOIs
StatePublished - Jul 2019
Event17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 - Shenzhen, China
Duration: Jul 1 2019Jul 3 2019

Publication series

Name2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019

Conference

Conference17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019
CountryChina
CityShenzhen
Period7/1/197/3/19

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Keywords

  • Dark Net Marketplace (DNM)
  • High-impact opioid product prediction
  • Key seller identification
  • Machine learning

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Information Systems

Cite this

Du, P. Y., Ebrahimi, M., Zhang, N., Chen, H., Brown, R. A., & Samtani, S. (2019). Identifying high-impact opioid products and key sellers in dark net marketplaces: An interpretable text analytics approach. In X. Zheng, A. Abbasi, M. Chau, A. Wang, & L. Zhou (Eds.), 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019 (pp. 110-115). [8823196] (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2019.8823196