If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles

TL;DR

This paper presents a machine-in-the-loop system that learns crime coverage archetypes and generates lede paragraphs for news articles about crimes in Los Angeles, aiming to enhance computational journalism.

Contribution

It introduces a probabilistic graphical model for learning article structures and a rule-based system for generating lede paragraphs in crime reporting.

Findings

Successfully learned crime article structures from news data

Generated coherent lede paragraphs using the rule-based system

Demonstrated potential for automating crime news coverage

Abstract

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.