  The prevalence and perniciousness of fake news have been a critical issue on
the Internet, which stimulates the development of automatic fake news detection
in turn. In this paper, we focus on evidence-based fake news detection, where
several evidences are utilized to probe the veracity of news (i.e., a claim).
Most previous methods first employ sequential models to embed the semantic
information and then capture the claim-evidence interaction based on attention
mechanisms. Despite their effectiveness, they still suffer from three
weaknesses. Firstly, sequential models fail to integrate the relevant
information that is scattered far apart in evidences. Secondly, they
underestimate much redundant information in evidences may be useless or
harmful. Thirdly, insufficient data utilization limits the separability and
reliability of representations captured by the model. To solve these problems,
we propose a unified Graph-based sEmantic structure mining framework with
ConTRAstive Learning, namely GETRAL in short. Specifically, we first model
claims and evidences as graph-structured data to capture the long-distance
semantic dependency. Consequently, we reduce information redundancy by
performing graph structure learning. Then the fine-grained semantic
representations are fed into the claim-evidence interaction module for
predictions. Finally, an adversarial contrastive learning module is applied to
make full use of data and strengthen representation learning. Comprehensive
experiments have demonstrated the superiority of GETRAL over the
state-of-the-arts and validated the efficacy of semantic mining with graph
structure and contrastive learning.
