  Despite recent advances in detecting fake news generated by neural models,
their results are not readily applicable to effective detection of
human-written disinformation. What limits the successful transfer between them
is the sizable gap between machine-generated fake news and human-authored ones,
including the notable differences in terms of style and underlying intent. With
this in mind, we propose a novel framework for generating training examples
that are informed by the known styles and strategies of human-authored
propaganda. Specifically, we perform self-critical sequence training guided by
natural language inference to ensure the validity of the generated articles,
while also incorporating propaganda techniques, such as appeal to authority and
loaded language. In particular, we create a new training dataset, PropaNews,
with 2,256 examples, which we release for future use. Our experimental results
show that fake news detectors trained on PropaNews are better at detecting
human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
