Data Collection During Periods of High Censorship Attempts

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During periods of heightened censorship, whether by governments, organizations, or private entities, a diverse range of data is collected by both the censors themselves and by groups working to monitor and circumvent these restrictions. This data paints a picture of the censorship landscape, revealing targets, methods, and impacts.

Data Collected by Censors:

Those implementing censorship aim to control information telegram data flow and typically collect data related to user activity and content. This can include:

IP Addresses and Connection Data: Censors often log the IP addresses of users attempting to access blocked content, as well as timestamps, connection durations, and even the unique identifiers of devices. This data can be used for surveillance, identification, and tracking of individuals or groups.
Domain Names and URLs: Records are kept of all attempted access to specific domain names and URLs that are on a blocklist. This helps censors refine their blocking mechanisms and identify new websites or content that require suppression.
Keywords and Content Filters: Systems monitor internet traffic for specific keywords, phrases, or types of content (e.g., political dissent, LGBTQ+ themes, news from independent sources). Data is collected on how often these keywords are detected, which users are attempting to access them, and the effectiveness of blocking them.
Application and Protocol Usage: Censors may monitor the use of specific applications, such as VPNs, encrypted messaging services, or circumvention tools. This data allows them to identify and block the protocols these tools use, making it harder for users to bypass restrictions. Deep Packet Inspection (DPI) is often employed to analyze the content of data packets, even if encrypted, to identify and block prohibited activities.
DNS Queries: Data on Domain Name System (DNS) queries can reveal what websites users are trying to visit. Censors can manipulate DNS responses to redirect users to block pages or simply prevent domain name resolution.
Social Media Activity: In environments with heavy social media censorship, data is collected on posts, shares, comments, and user interactions that are deemed objectionable. This can lead to algorithmic suppression, content takedowns, and even the identification of individuals for further action.
User Behavior and Fingerprinting: More sophisticated censorship systems may attempt to "fingerprint" user behavior to identify individuals or groups attempting to circumvent restrictions, even if their direct content access is masked. This involves analyzing traffic patterns, connection characteristics, and other metadata.
Data Collected by Anti-Censorship Organizations and Researchers:

Conversely, organizations dedicated to combating censorship collect data to understand its scope, methods, and impact, and to develop effective circumvention strategies. This data often includes:

Blocking Events and Anomalies: Researchers actively probe various networks and regions to detect instances of content blocking, throttling, or redirection. This involves measuring network reachability to thousands of websites and services, recording connection failures, unusual delays, or unexpected responses.
Censorship Techniques: Data is collected on the specific technical methods used by censors, such as DNS tampering, IP blocking, URL filtering, packet filtering, and connection resets. This helps in understanding the evolving nature of censorship and designing counter-measures.
Affected Content and Services: Comprehensive lists are compiled of websites, applications, and services that are being censored, often categorized by theme (e.g., news, human rights, social media, political content).
Geographical and Temporal Patterns: Data reveals where and when censorship events occur, allowing for the mapping of censorship landscapes and the identification of periods of increased suppression (e.g., during political unrest or elections).
Circumvention Tool Effectiveness: Information is gathered on which anti-censorship tools are effective in different censored environments, their performance, and their resilience to detection and blocking.
User Reports and Anecdotes: Crowdsourced data from affected users provides crucial qualitative information about their experiences with censorship, including personal impact, specific blockages, and the difficulties encountered.
Network Performance Data: To distinguish censorship from benign network issues (like outages or misconfigurations), organizations collect data on general network performance, latency, and throughput. This allows for more accurate attribution of disruptions to intentional censorship.
Metadata from Circumvention Tools: While prioritizing user privacy, some anti-censorship tools may collect anonymized and aggregated data about connection attempts, types of censorship encountered, and successful circumvention without revealing individual user identities.
The collection and analysis of this diverse data, from both sides of the censorship equation, are critical for understanding, mitigating, and ultimately challenging the ever-evolving landscape of information control.