  The sudden widespread menace created by the present global pandemic COVID-19
has had an unprecedented effect on our lives. Man-kind is going through
humongous fear and dependence on social media like never before. Fear
inevitably leads to panic, speculations, and the spread of misinformation. Many
governments have taken measures to curb the spread of such misinformation for
public well being. Besides global measures, to have effective outreach, systems
for demographically local languages have an important role to play in this
effort. Towards this, we propose an approach to detect fake news about COVID-19
early on from social media, such as tweets, for multiple Indic-Languages
besides English. In addition, we also create an annotated dataset of Hindi and
Bengali tweet for fake news detection. We propose a BERT based model augmented
with additional relevant features extracted from Twitter to identify fake
tweets. To expand our approach to multiple Indic languages, we resort to mBERT
based model which is fine-tuned over created dataset in Hindi and Bengali. We
also propose a zero-shot learning approach to alleviate the data scarcity issue
for such low resource languages. Through rigorous experiments, we show that our
approach reaches around 89% F-Score in fake tweet detection which supercedes
the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark
for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model
achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our
zero-shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali
Tweets without any annotated data, which clearly indicates the efficacy of our
approach.
