  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.
