  Fake news travels at unprecedented speeds, reaches global audiences and puts
users and communities at great risk via social media platforms. Deep learning
based models show good performance when trained on large amounts of labeled
data on events of interest, whereas the performance of models tends to degrade
on other events due to domain shift. Therefore, significant challenges are
posed for existing detection approaches to detect fake news on emergent events,
where large-scale labeled datasets are difficult to obtain. Moreover, adding
the knowledge from newly emergent events requires to build a new model from
scratch or continue to fine-tune the model, which can be challenging,
expensive, and unrealistic for real-world settings. In order to address those
challenges, we propose an end-to-end fake news detection framework named
MetaFEND, which is able to learn quickly to detect fake news on emergent events
with a few verified posts. Specifically, the proposed model integrates
meta-learning and neural process methods together to enjoy the benefits of
these approaches. In particular, a label embedding module and a hard attention
mechanism are proposed to enhance the effectiveness by handling categorical
information and trimming irrelevant posts. Extensive experiments are conducted
on multimedia datasets collected from Twitter and Weibo. The experimental
results show our proposed MetaFEND model can detect fake news on never-seen
events effectively and outperform the state-of-the-art methods.
