If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles
This is an incremental approach for journalists to potentially increase output in crime reporting, but it is a class project with limited scope.
The paper tackled the problem of automating crime story coverage in Los Angeles by developing a machine-in-the-loop system that learns prototypical article structures from classical news and generates lede paragraphs using police data, resulting in a system that can form article skeletons.
Developing and improving computational approaches to covering news can increase journalistic output and improve the way stories are covered. In this work we approach the problem of covering crime stories in Los Angeles. We present a machine-in-the-loop system that covers individual crimes by (1) learning the prototypical coverage archetypes from classical news articles on crime to learn their structure and (2) using output from the Los Angeles Police department to generate "lede paragraphs", first structural unit of crime-articles. We introduce a probabilistic graphical model for learning article structure and a rule-based system for generating ledes. We hope our work can lead to systems that use these components together to form the skeletons of news articles covering crime. This work was done for a class project in Jonathan May's Advanced Natural Language Processing Course, Fall, 2019.