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Record W2137428431 · doi:10.1145/1938606.1938609

The structure and content of online child exploitation networks

2010· article· en· W2137428431 on OpenAlex
Richard Frank, Bryce Westlake, Martin Bouchard

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWeb crawlerThe InternetEnforcementWorld Wide WebLaw enforcementComputer scienceInternet privacyWeb pageKey (lock)Social mediaNetwork structureFragmentation (computing)Content analysisAdvertisingComputer securityBusinessPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

The emergence of the Internet has provided people with the ability to find and communicate with others of common interests. Unfortunately, those involved in the practices of child exploitation have also received the same benefits. Although law enforcement continues its efforts to shut down websites dedicated to child exploitation, the problem remains uncurbed. Despite this, law enforcement has yet to examine these websites as a network and determine their structure, stability and susceptibleness to attack. We extract the structure and features of four online child exploitation networks using a custom-written webpage crawler. Social network analysis is then applied with the purpose of finding key players -- websites whose removal would result in the greatest fragmentation of the network and largest loss of hardcore material. Our results indicate that websites do not link based on the hardcore content of the target website; however, blogs do contain more hardcore content per page than non-blog websites.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.103

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.235
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations29
Published2010
Admission routes1
Has abstractyes

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