  User-generated content platforms curate their vast repositories into thematic
compilations that facilitate the discovery of high-quality material. Platforms
that seek tight editorial control employ people to do this curation, but this
process involves time-consuming routine tasks, such as sifting through
thousands of videos. We introduce Sifter, a system that improves the curation
process by combining automated techniques with a human-powered pipeline that
browses, selects, and reaches an agreement on what videos to include in a
compilation. We evaluated Sifter by creating 12 compilations from over 34,000
user-generated videos. Sifter was more than three times faster than dedicated
curators, and its output was of comparable quality. We reflect on the
challenges and opportunities introduced by Sifter to inform the design of
content curation systems that need subjective human judgments of videos at
scale.
