  Since fake news poses a serious threat to society and individuals, numerous
studies have been brought by considering text, propagation and user profiles.
Due to the data collection problem, these methods based on propagation and user
profiles are less applicable in the early stages. A good alternative method is
to detect news based on text as soon as they are released, and a lot of
text-based methods were proposed, which usually utilized words, sentences or
paragraphs as basic units. But, word is a too fine-grained unit to express
coherent information well, sentence or paragraph is too coarse to show specific
information. Which granularity is better and how to utilize it to enhance text
representation for fake news detection are two key problems. In this paper, we
introduce Elementary Discourse Unit (EDU) whose granularity is between word and
sentence, and propose a multi-EDU-structure awareness model to improve text
representation for fake news detection, namely EDU4FD. For the
multi-EDU-structure awareness, we build the sequence-based EDU representations
and the graph-based EDU representations. The former is gotten by modeling the
coherence between consecutive EDUs with TextCNN that reflect the semantic
coherence. For the latter, we first extract rhetorical relations to build the
EDU dependency graph, which can show the global narrative logic and help
deliver the main idea truthfully. Then a Relation Graph Attention Network
(RGAT) is set to get the graph-based EDU representation. Finally, the two EDU
representations are incorporated as the enhanced text representation for fake
news detection, using a gated recursive unit combined with a global attention
mechanism. Experiments on four cross-source fake news datasets show that our
model outperforms the state-of-the-art text-based methods.
