  The inability to correctly resolve rumours circulating online can have
harmful real-world consequences. We present a method for incorporating model
and data uncertainty estimates into natural language processing models for
automatic rumour verification. We show that these estimates can be used to
filter out model predictions likely to be erroneous, so that these difficult
instances can be prioritised by a human fact-checker. We propose two methods
for uncertainty-based instance rejection, supervised and unsupervised. We also
show how uncertainty estimates can be used to interpret model performance as a
rumour unfolds.
