Linguistic analysis of suspected child sexual offenders’ interactions in a dark web image exchange chatroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it