3 research outputs found
Automated Discovery of Internet Censorship by Web Crawling
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
Automated discovery of internet censorship by web crawling
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 and information that citizens are denied access to is atypical. To counter this, numerous techniques for maintaining URL filter lists have been proposed by various individuals, organisations and researchers. These aim to improve empirical data on censorship for benefit of the public and wider censorship research community, while also increasing the transparency of filtering activity by oppressive regimes. We present a new approach for discovering filtered domains in different target 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 April 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.</p