Over the last two decades, digital technologies have flattened old hierarchies in the news business and opened the conversation to a multitude of new voices. To help comprehend this promising but chaotic new public sphere, we're building a "social news machine" that will provide a structured view of the place where journalism meets social media. The basis of our project is a two-headed data ingest. On one side, all the news published online 24/7 by a sample group of influential US media outlets. On the other, all Twitter comments of the journalists who produced the stories. The two streams will be joined through network analysis and algorithmic inference. In future work we plan to expand the analysis to include all the journalism produced by major news outlets and the overall public response on Twitter, shedding new light on such issues as bias, originality, credibility, and impact.