Abstract: 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.
ACM Reference Format: Yan Chen, Andrés Monroy-Hernández, Ian Wehrman, Steve Oney, Walter S. Lasecki, and Rajan Vaish. 2020. Sifter: A Hybrid Workflow for Themebased Video Curation at Scale. In Woodstock ’18: ACM Symposium on Neural Gaze Detection, June 03–05, 2018, Woodstock, NY . ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn