  Misinformation spread over social media has become an undeniable infodemic.
However, not all spreading claims are made equal. If propagated, some claims
can be destructive, not only on the individual level, but to organizations and
even countries. Detecting claims that should be prioritized for fact-checking
is considered the first step to fight against spread of fake news. With
training data limited to a handful of languages, developing supervised models
to tackle the problem over lower-resource languages is currently infeasible.
Therefore, our work aims to investigate whether we can use existing datasets to
train models for predicting worthiness of verification of claims in tweets in
other languages. We present a systematic comparative study of six approaches
for cross-lingual check-worthiness estimation across pairs of five diverse
languages with the help of Multilingual BERT (mBERT) model. We run our
experiments using a state-of-the-art multilingual Twitter dataset. Our results
show that for some language pairs, zero-shot cross-lingual transfer is possible
and can perform as good as monolingual models that are trained on the target
language. We also show that in some languages, this approach outperforms (or at
least is comparable to) state-of-the-art models.
