  Extensive research on automatic fake news detection has been conducted due to
the significant detrimental effects of fake news proliferation. Most existing
approaches rely on a single source of evidence, such as comments or relevant
news, to derive explanatory evidence for decision-making, demonstrating
exceptional performance. However, their single evidence source suffers from two
critical drawbacks: (i) noise abundance, and (ii) resilience deficiency.
Inspired by the natural process of fake news identification, we propose an
Evidence-aware Multi-source Information Fusion (EMIF) network that jointly
leverages user comments and relevant news to make precise decision and excavate
reliable evidence. To accomplish this, we initially construct a co-attention
network to capture general semantic conflicts between comments and original
news. Meanwhile, a divergence selection module is employed to identify the
top-K relevant news articles with content that deviates the most from the
original news, which ensures the acquisition of multiple evidence with higher
objectivity. Finally, we utilize an inconsistency loss function within the
evidence fusion layer to strengthen the consistency of two types of evidence,
both negating the authenticity of the same news. Extensive experiments and
ablation studies on real-world dataset FibVID show the effectiveness of our
proposed model. Notably, EMIF shows remarkable robustness even in scenarios
where a particular source of information is inadequate.
