Leveraging Publicly Available Data to Discern Patterns of Human-Trafficking Activity

Bibliographic Data

ID2186155
AuthorsArtur Dubrawski (0000-0002-2372-0831, Carnegie Mellon University, corresponding author), Kyle Miller (0000-0002-3193-7926, Carnegie Mellon University), Matthew Barnes (0000-0002-0996-1954, Carnegie Mellon University), Benedikt Boecking (0000-0003-4822-0531, Carnegie Mellon University), Emily Kennedy (0000-0002-0963-9041), Emily B Kennedy (Carnegie Mellon University)
Year2015
Volume1
Issue1
Pages65-85
Publication date2015-01-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Human Trafficking (JOURNAL)
Journal identifiersISSN: 2332-2705 • E-ISSN: 2332-2713
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/23322705.2015.1015342
OpenAlexW1963579188
LanguageEN
Citations received18
References cited18

We present a few data analysis methods that can be used to process advertisements for escort services available in public areas of the Internet. These data provide a readily available proxy evidence for modeling and discerning human-trafficking activity. We show how it can be used to identify advertisements that likely involve such activity. We demonstrate its utility in identifying and tracking entities in the Web-advertisement data even if strongly identifiable features are sparse. We also show a few possible ways to perform community- and population-level analyses including behavioral summaries stratified by various types of activity and detection of emerging trends and patterns

Criminology · Data science · Human trafficking · Internet privacy · Machine learning · Population · The Internet · World Wide Web · Computer Science · Human Mobility and Location-Based Analysis · Medicine · Psychology · Sex work and related issues · Spam and Phishing Detection

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