  Manipulated news online is a growing problem which necessitates the use of
automated systems to curtail its spread. We argue that while misinformation and
disinformation detection have been studied, there has been a lack of investment
in the important open challenge of detecting harmful agendas in news articles;
identifying harmful agendas is critical to flag news campaigns with the
greatest potential for real world harm. Moreover, due to real concerns around
censorship, harmful agenda detectors must be interpretable to be effective. In
this work, we propose this new task and release a dataset, NewsAgendas, of
annotated news articles for agenda identification. We show how interpretable
systems can be effective on this task and demonstrate that they can perform
comparably to black-box models.
