Consuming news articles is an integral part of our daily lives and news agencies such as The Washington Post (WP) expend tremendous effort in providing high quality reading experiences for their readers. Journalists and editors are faced with the task of determining which articles will become popular so that they can efficiently allocate resources to support a better reading experience. The reasons behind the popularity of news articles are typically varied, and might involve contemporariness, writing quality, and other latent factors. In this paper, we cast the problem of popularity prediction problem as regression, engineer several classes of features (metadata, contextual or content-based, temporal, and social), and build models for forecasting popularity. The system presented here is deployed in a real setting at The Washington Post; we demonstrate that it is able to accurately predict article popularity with an R ≈ 0.8 using features harvested within 30 minutes of publication time.