  Social media has become the fulcrum of all forms of communication.
Classifying social texts such as fake news, rumour, sarcasm, etc. has gained
significant attention. The surface-level signals expressed by a social-text
itself may not be adequate for such tasks; therefore, recent methods attempted
to incorporate other intrinsic signals such as user behavior and the underlying
graph structure. Oftentimes, the `public wisdom' expressed through the
comments/replies to a social-text acts as a surrogate of crowd-sourced view and
may provide us with complementary signals. State-of-the-art methods on
social-text classification tend to ignore such a rich hierarchical signal.
Here, we propose Hyphen, a discourse-aware hyperbolic spectral co-attention
network. Hyphen is a fusion of hyperbolic graph representation learning with a
novel Fourier co-attention mechanism in an attempt to generalise the
social-text classification tasks by incorporating public discourse. We parse
public discourse as an Abstract Meaning Representation (AMR) graph and use the
powerful hyperbolic geometric representation to model graphs with hierarchical
structure. Finally, we equip it with a novel Fourier co-attention mechanism to
capture the correlation between the source post and public discourse. Extensive
experiments on four different social-text classification tasks, namely
detecting fake news, hate speech, rumour, and sarcasm, show that Hyphen
generalises well, and achieves state-of-the-art results on ten benchmark
datasets. We also employ a sentence-level fact-checked and annotated dataset to
evaluate how Hyphen is capable of producing explanations as analogous evidence
to the final prediction.
