  The quality of digital information on the web has been disquieting due to the
lack of careful manual review. Consequently, a large volume of false textual
information has been disseminating for a long time since the prevalence of
social media. The potential negative influence of misinformation on the public
is a growing concern. Therefore, it is strongly motivated to detect online
misinformation as early as possible. Few-shot-few-clue learning applies in this
misinformation detection task when the number of annotated statements is quite
few (called few shots) and the corresponding evidence is also quite limited in
each shot (called few clues). Within the few-shot-few-clue framework, we
propose a Bayesian meta-learning algorithm to extract the shared patterns among
different topics (i.e.different tasks) of misinformation. Moreover, we derive a
scalable method, i.e., amortized variational inference, to optimize the
Bayesian meta-learning algorithm. Empirical results on three benchmark datasets
demonstrate the superiority of our algorithm. This work focuses more on
optimizing parameters than designing detection models, and will generate fresh
insights into data-efficient detection of online misinformation at early
stages.
