Automatic Generation of Web Censorship Probe Lists
Bibliographic record
Abstract
Domain probe lists---used to determine which URLs to probe for Web censorship---play a critical role in Internet censorship measurement studies. Indeed, the size and accuracy of the domain probe list limits the set of censored pages that can be detected; inaccurate lists can lead to an incomplete view of the censorship landscape or biased results. Previous efforts to generate domain probe lists have been mostly manual or crowdsourced. This approach is time-consuming, prone to errors, and does not scale well to the ever-changing censorship landscape. In this paper, we explore methods for automatically generating probe lists that are both comprehensive and up-to-date for Web censorship measurement. We start from an initial set of 139,957 unique URLs from various existing test lists consisting of pages from a variety of languages to generate new candidate pages. By analyzing content from these URLs (i.e., performing topic and keyword extraction), expanding these topics, and using them as a feed to search engines, our method produces 119,255 new URLs across 35,147 domains. We then test the new candidate pages by attempting to access each URL from servers in eleven different global locations over a span of four months to check for their connectivity and potential signs of censorship. Our measurements reveal that our method discovered over 1,400 domains---not present in the original dataset---we suspect to be blocked. In short, automatically updating probe lists is possible, and can help further automate censorship measurements at scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".