Identifying community resources using data mining, crowdsourcing, and networked co-curation

The curatorial process is typically an expert led endeavor that requires extensive content review and judicious selection to assemble an archive of shared cultural value. Recent innovations in digital social curation open up new opportunities for non-expert participation in assembling collections, although challenges remain in terms of maintaining quality, straddling expert and amateur goals and integrating disparate and related efforts. In response, we present a flexible online web application designed to computationally support collective curatorial decision-making across diverse communities of interest. Findings from a 10-week deployment with a technology-arts community point to the utility of the system in accurately identifying and recommending useful content.