Dynamic LDA applied to ICLab longitudinal data for India (2016–2020) successfully identified 14 distinct censored topic clusters—including religious conflict, piracy, educational fraud, and political dissent—from 677 overtly-censored URLs out of 6,012 tested (11.3% overtly censored at least once). The model required monthly time-slice granularity; daily and weekly granularities produced unstable results due to wild swings in document counts.

D-LDA detected event-driven shifts in Indian censorship without prior knowledge: the word 'violence' disappeared from the 'Riots in India' topic cluster between months 6 and 14 of the measurement period, and 'killing' did not appear until month 16, consistent with the absence of actual riots during that window. Similarly, the 'Danish cartoonist' topic shifted from cartoon-focused discourse to broader Islamic-rights framing ('freedom,' 'speech') approximately 18 months in.

§3.2, Figure 3 evaluation

Data gaps severely degrade D-LDA accuracy: erasing every other month reduced the corpus from 4,577 to 1,919 documents and caused the model to lose detection of 'Religion-motivated killing,' 'Religious websites,' 'Muslim Violence,' and 'Homicide' topics entirely. Erasing one in three months (1,479 documents) caused further topic loss, and even removing one random month altered topic evolution trajectories. For 25% of pages, the gap between Wayback Machine snapshots and ICLab observations exceeds one year.

§3.3, Figures 4–5, Tables 2–3 evaluation

India's censorship apparatus, while less aggressive than China's, legally mandates ISP-level blocking capability and has deployed it regularly. Of 6,012 URLs in ICLab's India test list observed since 2016, only 677 (11.3%) were ever overtly censored (block-page redirect); the majority of anomalies were covert (connection disruption mimicking network faults) and excluded from analysis due to ambiguity. Censorship topics include not only political dissent but copyright enforcement, indicating infrastructure originally deployed for political control is routinely repurposed.

§2, §3.1 policy

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