Multi-word Chinese phrases as search seeds discover qualitatively different censored sites than individual English words: the phrase 'Chinese human rights violation' surfaces Chinese activist homepages and culture-specific outlets, while individual constituent words return only well-known Western media. TF-IDF scoring against a Chinese corpus ranks culturally rare phrases (e.g., '自由亚洲电台' / Radio Free Asia) as high-signal seeds and discards common filler phrases.

China actively censors websites far outside the popular-traffic tier: many discovered censored domains appear in the tail of the Alexa Top 1,000,000, and some are absent from Alexa entirely. This demonstrates the GFW pursues content-classified hosts regardless of traffic rank, not only high-visibility platforms.

§5.2, Figure 3 detection

The 1,125 newly discovered censored domains span a broad taxonomy: Chinese human rights organizations, Tibetan rights outlets, Falun Gong and religious freedom sites, minority news, privacy-enhancing technology providers, and sources covering Tiananmen and the 1989 democracy movement—none appearing on the Alexa Top 1,000 or FilteredWeb's blocklist. Privacy-enhancing technology providers appear explicitly as a censored category alongside political and religious content.

§1, §5.1, §6 detection

Using NLP phrase extraction on Chinese-language censored pages, the system discovered 1,125 new censored domains not present on any publicly available blocklist, producing a list 12.5× larger than the standard Citizen Lab list (220 web pages, 85 domains). Across three evaluations (unigrams, bigrams, trigrams, each capped at 1,000,000 URLs), only 3 of the top 50 discovered domains overlapped with FilteredWeb's top 50.

§5.1, Table 1 evaluation

Culturally specific Chinese phrases are strong predictors of censorship: unigrams for controversial figures—Wang Qishan (74%), Li Hongzhi (64%), Guo Boxiong (62%), Hu Jintao (56%)—returned the highest block rates. Trigrams such as 'Beidaihe meeting' (54%), 'CCP's religious policy' (42%), and 'Tiananmen Square demonstrations' (32%) showed similar patterns, confirming King et al.'s finding that references to collective political dissent are disproportionately targeted.

§5.3, Tables 2–4 detection

The paper proposes a black-box methodology for detecting censorship bias in LLMs by comparing responses to identical prompts in Simplified vs. Traditional Chinese — scripts for the same spoken language — controlling for translation quality while exploiting that Simplified Chinese training data is disproportionately sourced from mainland China's censored internet. Each prompt is repeated ten times and scored for similarity to censored text using an XLM-RoBERTa classifier fine-tuned on Baidu Baike (censored) vs. Chinese Wikipedia (uncensored) with scores from 0 to 1.

2024-ahmed-extended §2 / §2.4 keyword-filteringml-classifiermeasurement-platform

The paper proposes detecting translation censorship by back-translating the Chinese text to English via Google Translate, embedding each paragraph with distiluse-base-multilingual-cased-v1, and solving a linear-sum-assignment bipartite matching weighted by negated cosine similarity. Paragraphs below a similarity threshold are flagged as cut; matched paragraphs are recursively compared at sentence level to detect alterations.

2023-streisand-where §2 Methodology keyword-filteringmeasurement-platform

Hong Kong Twitter users are 33% more likely than a random control sample to have protected their accounts, and over 247% more likely to have deleted past Tweets, after enactment of the June 2020 national security law (NSL). These differences are statistically significant at p ≤ 1.74e-48 for all account-protection and Tweet-deletion metrics.

2023-wang-self-censorship §4.1 / Table 2 keyword-filteringmeasurement-platform

Hong Kong Twitter discussion of COVID-19 continued declining after mid-2020 and did not resurge during Hong Kong's large March 2022 COVID wave, unlike the control group whose COVID discussion tracked local transmission rates. The authors interpret this anomalous pattern as a generalized chilling effect: NSL legal risk suppressed even politically ambiguous health discussion that mainland China had censored but that may not clearly fall under the NSL.

2023-wang-self-censorship §4.2 / Figure 2c keyword-filteringmeasurement-platform

Of inaccessible Tweets from 2019, those containing NSL-sensitive political keywords are disproportionately deleted or protected by both Hong Kong users (36.38% inaccessibility) and Taipei users (34.89%), compared to New York City (29.45%) and Tokyo (30.80%). This suggests that NSL legal exposure — which extends extraterritorially under Article 38 — may be chilling speech even among users outside Hong Kong who transit or have ties to mainland China.

2023-wang-self-censorship §4.1 / Table 3 keyword-filteringmeasurement-platform

While Hong Kong users sharply reduced discussion of NSL-sensitive political topics after July 2020, their rate of Tweeting about non-sensitive topics (travel, food, art, media) remained stable and mirrored control-group trends. This targeted suppression — rather than a general withdrawal from Twitter — confirms the NSL produced specific self-censorship of covered speech rather than platform abandonment.

2023-wang-self-censorship §4.2 / Figure 2b keyword-filteringmeasurement-platform