Assessing the Validity of Automated Webcrawlers as Data Collection Tools to Investigate Online Child Sexual Exploitation
Bibliographic record
Abstract
The distribution of child sexual exploitation (CE) material has been aided by the growth of the Internet. The graphic nature and prevalence of the material has made researching and combating difficult. Although used to study online CE distribution, automated data collection tools (e.g., webcrawlers) have yet to be shown effective at targeting only relevant data. Using CE-related image and keyword criteria, we compare networks starting from CE websites to those from similar non-CE sexuality websites and dissimilar sports websites. Our results provide evidence that (a) webcrawlers have the potential to provide valid CE data, if the appropriate criterion is selected; (b) CE distribution is still heavily image-based suggesting images as an effective criterion; (c) CE-seeded networks are more hub-based and differ from non-CE-seeded networks on several website characteristics. Recommendations for improvements to reliable criteria selection are discussed.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".