The automated keyword-filtering tier is acknowledged to be largely ineffective at text classification due to well-known poor performance of keyword-matching approaches; the government compensates by deploying tens of thousands of human censors who manually review posts held by automated filters. The automated system affects large numbers of posts on fully two-thirds of Chinese social media sites surveyed.

Chinese censorship does not primarily target criticism of the state or its leaders — vitriol against top officials is routinely published. The decisive variable is collective action potential: posts that organize, incite, or reference crowd formation outside the Internet are censored regardless of whether they are pro- or anti-government, a distinction the authors formally establish and experimentally test for the first time.

Results detection

By operating their own Chinese social media site using commercially available software, documentation, and vendor support, the authors confirmed that censorship enforcement is delegated to platform operators via configurable off-the-shelf software. By default the software shipped with no automated review or blocking; webmasters activate keyword lists and gain controls for bulk deletion, IP blocking, user banning, and per-post-type restrictions.

Participant Observation deployment

Chinese social media censorship operates at two sequential stages: an instantaneous automated review that holds flagged posts before they receive a public URL, followed by human censors who read each held post and decide within roughly 24 hours whether to publish or delete it. The ex ante automated stage is invisible to observational methods that only monitor published content, creating a systematic blind spot in prior censorship measurement research.

Participant Observation / Fig. 2 detection

The Chinese censorship apparatus detects collective action potential through volume-burst monitoring: it identifies a spike in social media posts about a topic area, traces the spike to a real-world event, classifies the event as having collective action potential, and then censors all posts in that burst — regardless of individual post stance or content.

Theory / Collective Action Potential Hypothesis detection

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

DeepSeek, Kimi, and Doubao all transmit analytics logs to the same Autonomous System (AS24429, Zhejiang Taobao Network Co., Ltd., a ByteDance/Volcengine subsidiary), with one monitoring endpoint IP directly overlapping between DeepSeek and Doubao. Additionally, all four non-Qwen services maintain connections with servers physically located in China throughout the chat session, transmitting user IDs, session IDs, viewport data, language preferences, and in Baidu-Chat's case, the full query text via URL-encoded CAPTCHA requests.

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

All five Chinese LLM services transmit partial or complete responses to the client machine even when output blocking is triggered, representing a major information leak. For DeepSeek and Qwen, truncated blocked responses are on average close in token length to full successful responses. For Baidu-Chat, the complete response is transmitted to the client but only partially rendered in the browser UI, with only a word or two visible on screen.

2026-ablove-characterizing §VI-C keyword-filtering