  The proliferation of social media platforms has fueled the rapid
dissemination of fake news, posing threats to our real-life society. Existing
methods use multimodal data or contextual information to enhance the detection
of fake news by analyzing news content and/or its social context. However,
these methods often overlook essential textual news content (articles) and
heavily rely on sequential modeling and global attention to extract semantic
information. These existing methods fail to handle the complex, subtle twists
in news articles, such as syntax-semantics mismatches and prior biases, leading
to lower performance and potential failure when modalities or social context
are missing. To bridge these significant gaps, we propose a novel multi-hop
syntax aware fake news detection (MSynFD) method, which incorporates
complementary syntax information to deal with subtle twists in fake news.
Specifically, we introduce a syntactical dependency graph and design a
multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It
extends the effect of word perception, leading to effective noise filtering and
adjacent relation enhancement. Subsequently, a sequential relative
position-aware Transformer is designed to capture the sequential information,
together with an elaborate keyword debiasing module to mitigate the prior bias.
Extensive experimental results on two public benchmark datasets verify the
effectiveness and superior performance of our proposed MSynFD over
state-of-the-art detection models.
