In four of five incidents (all except Syria), spam accounts were registered in temporally clustered blocks while legitimate accounts were not; in Russia and Mexico, multiple distinct registration bursts were observed. Across all five incidents, spam account usernames were automatically generated, with China'12 and Mexico accounts following a {name}{name}{number} pattern padded to exactly 15 characters (Twitter's maximum), making algorithmic reverse-engineering feasible.

In the Russia and Mexico incidents, spam tweets showed statistically significant spikes at fixed sub-hour intervals (5 and 15 minutes past the hour respectively), consistent with cron-job automation. Despite this automation, both campaigns deliberately mimicked human diurnal activity patterns — spam volume peaked at the same hours as legitimate traffic — to evade time-based anomaly detection.

§3.2, Figures 2–3 detection

Default-profile usage was significantly elevated among spam accounts in China'11 (89.4% spam vs 51.2% non-spam), Russia (57.8% vs 34.7%), and China'12 (95.1% vs 47.8%); however, Mexico inverted this trend with only 1.7% of spam accounts using default profiles vs 27.0% of non-spam accounts, indicating that newer campaigns actively customize profiles to evade appearance-based detection.

§4.2, Table 7 detection

Across five political spam incidents, spam constituted 62–73% of all tweets in the Russia, China'12, and Mexico incidents, while Syria had only 6% spam. In the China'12 incident, 1,700 spam accounts (14% of all accounts) generated 600,000 spam tweets (73% of total), with 10 individual accounts each producing over 5,000 tweets before shutdown; in Mexico, 50 accounts sustained 1,000 spam tweets per day throughout the incident.

§2, Table 2 evaluation

Twitter's existing automated spam-filtering mechanisms caught only approximately 50% of politically motivated spam in the Russian parliamentary election incident, as reported by Thomas et al. (2012) and noted as the baseline for this study. Spammer behavior varied sufficiently across incidents (targeting strategy, URL usage, mention patterns, default-profile adoption) that supervised machine-learning classifiers trained on one incident are unlikely to generalize to others.

§1, §5 evaluation

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