Public Good Software's products match journalistic articles and other narrative content to relevant charitable causes and nonprofit organizations so that readers can take action on the issues raised by the articles' publishers. Previously an expensive and labor-intensive process, application of machine learning and other automated textual analyses now allow us to scale this matching process to the volume of content produced daily by multiple large national media outlets. This paper describes the development of a layered system of tactics working across a general news model that minimizes the need for human curation while maintaining the particular focus of concern for each individual publication. We present a number of general strategies for categorizing heterogenous texts, and suggest editorial and operational tactics for publishers to make their publications and individual content items more efficiently analyzed by automated systems.
@article{arxiv.1710.08522,
title = {Automating, Operationalizing and Productizing Journalistic Article Analysis},
author = {Eric Kingery and Michael S. Manley and Daniel Ratner},
journal= {arXiv preprint arXiv:1710.08522},
year = {2017}
}