Server-side keyword enumeration on Chinese platforms has become increasingly uneconomical: platforms now require non-virtual phone numbers for account registration, and test accounts are banned after sending a threshold volume of sensitive content. The paper's 5,521-article dataset and 1,956 confirmed keyword combinations were collected via sample testing between September 2017 and October 2018, with registration costs being the primary limiting factor for research scale.

The component-aware binary splitting algorithm (CompAwareBinSplit) requires on average 35.47 messages per article to isolate a sensitive keyword combination — 10.3% as many as the 342.72 required by the previously used algorithm — and is the only evaluated algorithm that correctly handles overlapping keyword components and multiple co-occurring combinations.

§5.4, §6, Table 1 evaluation

WeChat, Alibaba Wangwang, Zhihu, and Sina Weibo all implement keyword combination filtering — messages are blocked only when every component of a blacklisted combination appears simultaneously, regardless of order. This allows censors to target sensitive contexts (e.g., 习近平 + 三连任 [Xi Jinping + three consecutive terms]) without filtering neutral mentions of individual terms.

§3 detection

The previously used bisection algorithm required an average of 342.72 messages per news article to isolate a triggering keyword combination, and produced incorrect results in 44% of test cases — primarily because the Unilateral Elimination Flaw caused it to miss components that appeared multiple times in an article.

§4, §6 evaluation

WeChat censors messages even when keyword components overlap within the message text — e.g., the combination 帶來 + 調整 + 整體 + 領域 triggers filtering in the fused form 帶來abc調整體xyz領域 where 調整 and 整體 share a character. No previously published algorithm correctly identified overlapping components; only CompAwareBinSplit resolves this by advancing the search window from index i+1 rather than past the full matched span.

§4.2, §5.4 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