Linguistic analysis of suspected child sexual offenders’ interactions in a dark web image exchange chatroom


Child sexual offenders convene in dark web spaces to exchange indecent imagery, advice and support. In response, law enforcement agencies deploy undercover agents to pose as offenders online to gather intelligence on these offending communities. Currently, however, little is known about how offenders interact online, which raises significant questions around how undercover officers should ‘authentically’ portray the persona of a child sexual offender. This article presents the first linguistic description of authentic offender–offender interactions taking place on a dark web image exchange chatroom. Using move analysis, we analyse chatroom users’ rhetorical strategies. We then model the move sequences of different users and user types using Markov chains, to make comparisons between their linguistic behaviours. We find the predominant moves characterising this chatroom are Offering Indecent Images, Greetings, Image Appreciation, General Rapport and Image Discussion, and that rhetorical strategies differ between users of different levels of offending and dark web image-sharing experience.

Publication DOI:
Divisions: College of Business and Social Sciences > School of Social Sciences & Humanities
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College of Business and Social Sciences > Aston Institute for Forensic Linguistics
Additional Information: Available only under the terms of the Creative Commons CC BY-NC-ND licence. Funding: This work was supported by the Alan Turing Institute under the Defence & Security programme.
Uncontrolled Keywords: Child sexual abuse,Dark web,Indecent images of children,Move analysis,Rhetorical structure,Undercover police,Linguistics and Language,Law
Publication ISSN: 1748-8893
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://journal ... icle/view/41446 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-05-21
Accepted Date: 2021-01-18
Authors: Chiang, Emily (ORCID Profile 0000-0002-0216-1719)
Nguyen, Dong
Towler, Amanda
Haas, Mark
Grieve, Jack



Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 21 May 2023.

License: Creative Commons Attribution Non-commercial No Derivatives

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