A maximum entropy named entity extraction (NEE) model trained on Chinese-language Wikipedia achieved 89.63% recall and 83.44% specificity for person names, 96.3% recall and 69.80% specificity for place names, and 87.56% recall and 88.40% specificity for organization names. Despite 0.42% precision for person names, the system reduces the number of words requiring censorship probes by nearly an order of magnitude while retaining nearly 90% of actual named entities.

During a two-month run in 2011 that coincided with the Jasmine Revolution protests, China's HTTP GET request backbone blacklist showed no additions or removals of keywords on a daily, weekly, or even monthly basis. Numerous current-event terms that triggered search engine censorship produced zero GET request RST responses, indicating the two censorship mechanisms operate on entirely different update timescales.

§4.2 evaluation

To measure Chinese search engine censorship independently of backbone GET request filtering, the authors split each search engine HTTP GET request across multiple TCP packets so the server would reassemble the full query but routers performing single-packet keyword inspection would not see a complete match. This technique allowed ground-truth measurement of search engine responses free of backbone RST injection interference.

§3 defense

A controlled probe of two Chinese search engines found that the query 'fuck' triggered a legal notice that results had been removed, while 'fuck you' did not, suggesting that search engine censorship suppresses websites where a sensitive term appears prominently rather than matching exact byte strings in the query itself. The paper concludes this mechanism is topical and website-removal-based, not a static keyword blacklist.

§4.2 detection

During the 2011 Jasmine Revolution, words such as 'Jasmine Flower,' terms linked to Liu Xiaobo's Nobel Prize, and numeric references to presidential rent criticism triggered Chinese search engine censorship (results-removed warnings) but produced no HTTP GET request RST injections. This demonstrates that search engine filtering and backbone keyword filtering are independently operated layers that diverge sharply for rapidly evolving current-event content.

§4.2 detection

The largest single source of censored domains in the GNL is MESA lab's SNI monitoring dataset (E21-SNI-Top200w.txt) containing 57,362 censored domains, and E21-SNI-Top120W-20221020.txt with 36,467 domains—totaling over 93K domains from network tap data alone for a single country (E21 = Ethiopia per InterSecLab attribution). A separate Xinjiang dataset (XJ-CUCC-SNI-Top200w.txt) contains 13,604 domains. These datasets "do not seem to come from popular domain lists, and instead appear to be gathered from network taps," confirming that Geedge builds censorship target lists directly from passive traffic observation.

2026-sheffey-geedge §4.2, Table 3 dpisni-blockingmeasurement-platform

Of 6,915,266 domains extracted from the 572 GiB Geedge Networks Leak (GNL), 298,955 censored domains (93.7% of all GNL-censored domains) appear in neither Tranco top-1M nor CitizenLab test lists. Measurements across China (Guangzhou/Nanjing), Myanmar, Pakistan, and Algeria confirmed censorship via DNS injection and SNI-based TLS connection termination. The GNL covers 25–62% of Tranco-censored domains across countries, showing substantial but incomplete overlap. This vendor-side ground truth reveals a censorship surface roughly two orders of magnitude larger than curated academic test lists.

2026-sheffey-geedge §4.1, Table 2 dpisni-blockingdns-poisoningmeasurement-platform

CensorAlert aggregates censorship signals from heterogeneous open sources — including OONI, Cloudflare Radar, NetBlocks, GitHub Issues of circumvention tools (Hysteria, Xray), Net4People BBS, NTC Party forum, Mastodon, X, Telegram channels, and arXiv — normalizing each item into a common schema preserving timestamp, source, raw data, and provenance with a link to the original content.

2026-zohaib-extended §2 (Data Sources) measurement-platform

Systematic measurement platforms (OONI, Censored Planet, Cloudflare Radar, NetBlocks) have inherent blind spots due to geographic coverage and protocol-specific test constraints; critical censorship discoveries — including GFW fully-encrypted protocol blocking and regional Chinese censorship — were first surfaced by user reports on forums and GitHub issue pages of circumvention tools, not by automated measurement infrastructure.

2026-zohaib-extended §1 measurement-platformfully-encrypted-detect

CensorAlert generates text embeddings (OpenAI text-embedding-3-small) from each item's summary, title, and tags to cluster semantically similar reports within configurable time windows; near-duplicates such as reposts, copied headlines, and translations are collapsed into a single canonical post that preserves all original source URLs and metadata.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform

CensorAlert's LLM agent scores each ingested item 0–5 on five independent dimensions — credibility, novelty, impact, timeliness, and verifiability — then computes a normalized significance score (0–10) from these components; items are processed every two hours using OpenAI GPT-5 Thinking (hosted on Azure) constrained to return structured JSON output.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform