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.

In China, it is typically the publisher (not the government) who self-censors translated books to avoid punishment including harsh scrutiny of future publications, book confiscation, and suspension of publishing rights. Authors are often unaware their translations were altered until well after publication. This self-censorship dynamic produces more restrictive outcomes than direct government censorship because publishers err on the side of caution without clear rules.

§1 Introduction policy

A case study on Chapter 5 of Chinese Literature: A Very Short Introduction (Knight) found 7 censored topics, 5 removed paragraphs, 31 removed sentences, and 2 removed/altered words in the Chinese translation. Censored topics included 2000 Nobel laureate Gao Xingjian, the Tiananmen Square Massacre, Mao Zedong, the Cultural Revolution, the Great Leap Forward, the plasma economy scandal in Henan, and a discussion of book censorship itself.

§3 Case Study / Table 1 evaluation

ChatGPT correctly identified missing sentences in a partially censored translation and correctly judged a complete translation as complete in a control condition, demonstrating that LLMs are a viable complementary detection method. The paper notes that having multiple independent detection approaches (NLP alignment, bitext mining, LLM-based reasoning) improves overall robustness by enabling cross-validation.

§4 Discussion / Appendix C detection

The paper argues that an effective counter to translation censorship is to actively trigger the Streisand effect: publishing detected censored content side-by-side with the original on a public website causes the censored text to reach a broader audience — including people who would not have read the censored version — and makes the censorship itself backfire. Censors deliberately avoid publicizing removals precisely to prevent this outcome.

§1 Introduction / §5 Conclusion defense

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

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

After the NSL entered into force in July 2020, the proportion of Hong Kong Tweets containing NSL-sensitive political keywords declined steadily and never returned to prior levels. By contrast, the control group's equivalent keyword usage rebounded (e.g., surging around the August 2021 Taliban takeover and March 2022 Ukraine invasion), indicating the Hong Kong decline is attributable to legal chilling rather than global topic cycles.

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