  Propaganda is a form of deceptive narratives that instigate or mislead the
public, usually with a political purpose. In this paper, we aim to identify
propaganda in political news at two fine-grained levels: sentence-level and
token-level. We observe that propaganda content is more likely to be embedded
in sentences that attribute causality or assert contrast to nearby sentences,
as well as seen in opinionated evaluation, speculation and discussions of
future expectation. Hence, we propose to incorporate both local and global
discourse structures for propaganda discovery and construct two teacher models
for identifying PDTB-style discourse relations between nearby sentences and
common discourse roles of sentences in a news article respectively. We further
devise two methods to incorporate the two types of discourse structures for
propaganda identification by either using teacher predicted probabilities as
additional features or soliciting guidance in a knowledge distillation
framework. Experiments on the benchmark dataset demonstrate that leveraging
guidance from discourse structures can significantly improve both precision and
recall of propaganda content identification.
