Automated Discovery of Internet Censorship by Web Crawling

TL;DR

This paper introduces an automated web crawling system to discover and map censored domains across different countries, significantly expanding existing filter lists and revealing the interconnected nature of censored content.

Contribution

The authors develop a fully automated, no-human-interaction method for identifying filtered domains using web crawling, outperforming existing techniques and creating larger, more comprehensive censorship datasets.

Findings

Successfully identified more filtered domains than existing methods.

Built domain filter lists an order of magnitude larger than public lists as of Jan 2018.

Mapped the interconnected network of censored web resources.

Abstract

Censorship of the Internet is widespread around the world. As access to the web becomes increasingly ubiquitous, filtering of this resource becomes more pervasive. Transparency about specific content that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals and organisations that aim to empirical data on censorship for benefit of the public and wider censorship research community. We present a new approach for discovering filtered domains in different countries. This method is fully automated and requires no human interaction. The system uses web crawling techniques to traverse between filtered sites and implements a robust method for determining if a domain is filtered. We demonstrate the effectiveness of the approach by running experiments to search for filtered content in four different censorship regimes. Our results show that we perform better than the current state of the art and have built domain filter lists an order of magnitude larger than the most widely available public lists as of Jan 2018. Further, we build a dataset mapping the interlinking nature of blocked content between domains and exhibit the tightly networked nature of censored web resources.