Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA)

Paul A. Watters, Nigel Phair

Research output: A Conference proceeding or a Chapter in BookConference contribution

7 Citations (Scopus)

Abstract

While social media is a new and exciting technology, it has the potential to be misused by organized crime groups and individuals involved in the illicit drugs trade. In particular, social media provides a means to create new marketing and distribution opportunities to a global marketplace, often exploiting jurisdictional gaps between buyer and seller. The sheer volume of postings presents investigational barriers, but the platform is amenable to the partial automation of open source intelligence. This paper presents a new methodology for automating social media data, and presents two pilot studies into its use for detecting marketing and distribution of illicit drugs targeted at Australians. Key technical challenges are identified, and the policy implications of the ease of access to illicit drugs are discussed.

Original languageEnglish
Title of host publicationCyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings
PublisherSpringer
Pages66-76
Number of pages11
Volume7672
ISBN (Print)9783642353611
DOIs
Publication statusPublished - 2012
Event4th International Symposium on Cyberspace Safety and Security, CSS 2012 - Melbourne, VIC, Australia
Duration: 12 Dec 201213 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7672 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference4th International Symposium on Cyberspace Safety and Security, CSS 2012
CountryAustralia
CityMelbourne, VIC
Period12/12/1213/12/12

Fingerprint

Social Media
Marketing
Drugs
Crime
Automation
Open Source
Partial
Methodology
Intelligence

Cite this

Watters, P. A., & Phair, N. (2012). Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA). In Cyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings (Vol. 7672, pp. 66-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7672 LNCS). Springer. https://doi.org/10.1007/978-3-642-35362-8_7
Watters, Paul A. ; Phair, Nigel. / Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA). Cyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings. Vol. 7672 Springer, 2012. pp. 66-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Watters, PA & Phair, N 2012, Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA). in Cyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings. vol. 7672, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7672 LNCS, Springer, pp. 66-76, 4th International Symposium on Cyberspace Safety and Security, CSS 2012, Melbourne, VIC, Australia, 12/12/12. https://doi.org/10.1007/978-3-642-35362-8_7

Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA). / Watters, Paul A.; Phair, Nigel.

Cyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings. Vol. 7672 Springer, 2012. p. 66-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7672 LNCS).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Watters PA, Phair N. Detecting illicit drugs on social media using Automated Social Media Intelligence Analysis (ASMIA). In Cyberspace Safety and Security - 4th International Symposium, CSS 2012, Proceedings. Vol. 7672. Springer. 2012. p. 66-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-35362-8_7