  The ability to continuously discover domain-specific content from the Web is
critical for many applications. While focused crawling strategies have been
shown to be effective for discovery, configuring a focused crawler is difficult
and time-consuming. Given a domain of interest $D$, subject-matter experts
(SMEs) must search for relevant websites and collect a set of representative
Web pages to serve as training examples for creating a classifier that
recognizes pages in $D$, as well as a set of pages to seed the crawl. In this
paper, we propose DISCO, an approach designed to bootstrap domain-specific
search. Given a small set of websites, DISCO aims to discover a large
collection of relevant websites. DISCO uses a ranking-based framework that
mimics the way users search for information on the Web: it iteratively
discovers new pages, distills, and ranks them. It also applies multiple
discovery strategies, including keyword-based and related queries issued to
search engines, backward and forward crawling. By systematically combining
these strategies, DISCO is able to attain high harvest rates and coverage for a
variety of domains. We perform extensive experiments in four social-good
domains, using data gathered by SMEs in the respective domains, and show that
our approach is effective and outperforms state-of-the-art methods.
