Identifying online risk markers of hard-to-observe crimes through semi-inductive triangulation: The case of human trafficking in the United States

Many types of crime are difficult to study because they are hard to operationalize, hidden from the public, or both. With communication increasingly moving to online domains, recent work has begun to examine whether the online domain contains traces of such hard-to-observe crimes. This study explore...

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Bibliographic Details
Main Author: Vries, Ieke de (Author)
Contributors: Radford, Jason
Format: Electronic Article
Language:English
Published: 2022
In: The British journal of criminology
Year: 2022, Volume: 62, Issue: 3, Pages: 639-658
Online Access: Volltext (lizenzpflichtig)
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Keywords:
Computational criminology
Computational methods
Human Trafficking
Online data
mixed methods
Description
Summary:Many types of crime are difficult to study because they are hard to operationalize, hidden from the public, or both. With communication increasingly moving to online domains, recent work has begun to examine whether the online domain contains traces of such hard-to-observe crimes. This study explores the online linguistic contours of hard-to-observe crimes through a rigorous mixed-methods approach that combines interviews and computational text analysis. Using human trafficking in illicit massage businesses as a proof-of-concept, we show how this approach, which we call semi-inductive triangulation, meets the empirical contextuality and relationality of crime traces in the online domain. The findings contribute to an emerging field of computational criminology and call for an integration of linguistic approaches in criminology.
ISSN:1464-3529
DOI:10.1093/bjc/azab077