  Journalists must find stories in huge amounts of textual data (e.g. leaks,
bills, press releases) as part of their jobs: determining when and why text
becomes news can help us understand coverage patterns and help us build
assistive tools. Yet, this is challenging because very few labelled links
exist, language use between corpora is very different, and text may be covered
for a variety of reasons. In this work we focus on news coverage of local
public policy in the San Francisco Bay Area by the San Francisco Chronicle.
First, we gather news articles, public policy documents and meeting recordings
and link them using probabilistic relational modeling, which we show is a
low-annotation linking methodology that outperforms other retrieval-based
baselines. Second, we define a new task: newsworthiness prediction, to predict
if a policy item will get covered. We show that different aspects of public
policy discussion yield different newsworthiness signals. Finally we perform
human evaluation with expert journalists and show our systems identify policies
they consider newsworthy with 68% F1 and our coverage recommendations are
helpful with an 84% win-rate.
