  This work outlines how we prioritize original news, a critical indicator of
news quality. By examining the landscape and life-cycle of news posts on our
social media platform, we identify challenges of building and deploying an
originality score. We pursue an approach based on normalized PageRank values
and three-step clustering, and refresh the score on an hourly basis to capture
the dynamics of online news. We describe a near real-time system architecture,
evaluate our methodology, and deploy it to production. Our empirical results
validate individual components and show that prioritizing original news
increases user engagement with news and improves proprietary cumulative
metrics.
