Comparing 5.6M pre-coup tweets (2015 Turkish general election) to 8.5M post-coup tweets (July–November 2016), the authors found 72% fewer government-censored tweets post-coup (142,492 vs. 513,719), with an estimated 43% of that decline attributable to reduced overall Twitter usage in Turkey and the remainder to user self-censorship.

41% of users (139,042 of 342,650) in the post-coup dataset voluntarily removed 18% of all post-coup tweets by switching to protected mode, deleting accounts, or deleting individual tweets; the largest groups were active users who deleted some tweets (44% of affected accounts) and users who switched to protected mode (22%).

§4.1, Table 1 policy

Zero pro-Gülen topics appeared in the public tweet set post-coup, while 70% of unreachable (deleted/protected) Gülen-related tweets were pro-Gülen; the unreachable rate for Gülen-related tweets was twice the background rate, quantifying rapid directional self-censorship on politically targeted content within days of a government crackdown.

§4.2–§4.3, Table 2 policy

Tor usage in Turkey spiked sharply during the initial days of the July 2016 coup—when ISPs were actively throttling Twitter—but declined steadily in subsequent months back toward pre-coup baselines, consistent with post-coup suppression being driven by chilling effects rather than sustained network-level blocking.

§4.1, Figures 2–3 evaluation

Twitter's official Transparency Report for July–December 2016 reported 489 censored tweets in Turkey from non-withheld accounts; the authors identified 6,402 unique censored tweets from the same period—approximately 13× more than officially reported—replicating an earlier order-of-magnitude undercount finding by Tanash et al. (2015).

§5 evaluation

The GFW's robustness depends principally on suppressed citizen demand for uncensored information, not solely on access barriers. Calibration shows censorship remains stable even if the unencouraged access rate were substantially expanded, because low demand and moderate social transmission prevent information from reaching population-wide tipping points. However, censorship is fragile to demand stimulation: scaling the encouragement intervention to all students would, per the model, inform the entire student population.

2019-chen-impact §V ip-blockingkeyword-filteringthrottling

During the June 2025 Iran shutdown, circumvention tool performance diverged sharply by transport design. Psiphon's multi-protocol architecture sustained 1.5 million concurrent users—roughly one-third of its normal Iranian base. Lantern's "proxyless" protocol (domain-fronting via CDN, ~40% of Lantern's Iranian traffic) showed moderate success. Tor usage collapsed during the blackout but bridge connections surged and rebounded quickly after lifting. BeePass (serving 500k+ daily users at shutdown onset) used live A/B testing of port/obfuscation-prefix combinations to probe the censors' blocking parameters in real time. The Ceno Browser's P2P network grew from 600 active peers on June 13 to ~8,000 by July 11, indicating that decentralized fallback paths stayed up even during peak blocking.

2025-iran-shutdown-measurement §Tool Performance dpithrottling

During the June 2025 shutdown, Iranian authorities blocked international One-Time Password (OTP) SMS delivery, preventing new sign-ins to foreign secure-messaging platforms and VPN services. This forced users toward government-approved domestic platforms that lack security and privacy protections. The blockade of OTPs effectively weaponized account-recovery flows as a secondary shutdown layer, disproportionately affecting users who needed to activate new circumvention tools during the crisis.

2025-iran-shutdown-measurement §Human Rights Implications port-blockingdpikeyword-filtering

Simple character-level perturbations (English) and homophone substitutions (Chinese), combined with LLM instruction-following prompts directing the model to use word substitutions in its output, successfully bypassed all input and output filters for all 41 input-blocked and 197 output-blocked queries across five major Chinese LLM services (Baidu-Chat, DeepSeek, Doubao, Kimi, Qwen). Every input-blocked query contained at least one keyword combination that alone triggered the filter, confirming keyword-matching rather than semantic classification.

2026-ablove-characterizing §VII-D keyword-filtering

Cross-national experiments conducted from Singapore, South Korea, and Taiwan during February 19–24, 2025 found no variance in blocking implementations, event syntax, or server infrastructure across all five Chinese LLM services. Input blocking was enacted identically in all three international locations, and services connected to the exact same server IP addresses globally — Kimi and Baidu-Chat connected to identical IPs and DeepSeek to the same two addresses across all tested locations.

2026-ablove-characterizing §VIII-A keyword-filtering

Input blocking in Chinese LLM services (DeepSeek, Qwen, Kimi, Doubao) is overwhelmingly consistent: all four services persistently block the exact same queries across all 5 measurement samples in both Simplified and Traditional Chinese. Output blocking is far less consistent, with only 29 out of 349 output-blocked queries blocked across all 5 samples. Baidu-Chat is exceptional: it performs almost no input blocking but instead relies heavily on post-search and output blocking (78.6% of blocks are output-phase).

2026-ablove-characterizing §VI-B keyword-filteringdpi