  Most fake news detection methods learn latent feature representations based
on neural networks, which makes them black boxes to classify a piece of news
without giving any justification. Existing explainable systems generate
veracity justifications from investigative journalism, which suffer from
debunking delayed and low efficiency. Recent studies simply assume that the
justification is equivalent to the majority opinions expressed in the wisdom of
crowds. However, the opinions typically contain some inaccurate or biased
information since the wisdom of crowds is uncensored. To detect fake news from
a sea of diverse, crowded and even competing narratives, in this paper, we
propose a novel defense-based explainable fake news detection framework.
Specifically, we first propose an evidence extraction module to split the
wisdom of crowds into two competing parties and respectively detect salient
evidences. To gain concise insights from evidences, we then design a
prompt-based module that utilizes a large language model to generate
justifications by inferring reasons towards two possible veracities. Finally,
we propose a defense-based inference module to determine veracity via modeling
the defense among these justifications. Extensive experiments conducted on two
real-world benchmarks demonstrate that our proposed method outperforms
state-of-the-art baselines in terms of fake news detection and provides
high-quality justifications.
