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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VerfasserInnen: Vries, Ieke de (Verfasst von) ; Radford, Jason (Verfasst von)
Medienart: Elektronisch Aufsatz
Sprache:Englisch
Veröffentlicht: 2022
In: The British journal of criminology
Jahr: 2022, Band: 62, Heft: 3, Seiten: 639-658
Online-Zugang: Volltext (lizenzpflichtig)
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Schlagwörter:
Computational criminology
Computational methods
mixed methods
Online data
Human Trafficking
Beschreibung
Zusammenfassung: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