  Domain probe lists--used to determine which URLs to probe for Web
censorship--play a critical role in Internet censorship measurement studies.
Indeed, the size and accuracy of the domain probe list limits the set of
censored pages that can be detected; inaccurate lists can lead to an incomplete
view of the censorship landscape or biased results. Previous efforts to
generate domain probe lists have been mostly manual or crowdsourced. This
approach is time-consuming, prone to errors, and does not scale well to the
ever-changing censorship landscape.
  In this paper, we explore methods for automatically generating probe lists
that are both comprehensive and up-to-date for Web censorship measurement. We
start from an initial set of 139,957 unique URLs from various existing test
lists consisting of pages from a variety of languages to generate new candidate
pages. By analyzing content from these URLs (i.e., performing topic and keyword
extraction), expanding these topics, and using them as a feed to search
engines, our method produces 119,255 new URLs across 35,147 domains. We then
test the new candidate pages by attempting to access each URL from servers in
eleven different global locations over a span of four months to check for their
connectivity and potential signs of censorship. Our measurements reveal that
our method discovered over 1,400 domains--not present in the original
dataset--we suspect to be blocked. In short, automatically updating probe lists
is possible, and can help further automate censorship measurements at scale.
