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.

本文提出了一种基于 BERT 模型的方法，结合社交媒体中提取的相关特征，可以在多种印度语言，包括英语以外的语言中，尽早检测 COVID-19 的虚假消息，并提出了一种零样本学习方法用于数据稀缺问题。经过严格实验，展示了该方法在虚假消息检测上的有效性，达到 89% 的 F 值，在 Hindi 和 Bengali Tweets 中初步建立了基准，分别达到 79% 和 81% 的 F 值。