  As the COVID-19 pandemic sweeps across the world, it has been accompanied by
a tsunami of fake news and misinformation on social media. At the time when
reliable information is vital for public health and safety, COVID-19 related
fake news has been spreading even faster than the facts. During times such as
the COVID-19 pandemic, fake news can not only cause intellectual confusion but
can also place lives of people at risk. This calls for an immediate need to
contain the spread of such misinformation on social media. We introduce CTF,
the first COVID-19 Twitter fake news dataset with labeled genuine and fake
tweets. Additionally, we propose Cross-SEAN, a cross-stitch based
semi-supervised end-to-end neural attention model, which leverages the large
amount of unlabelled data. Cross-SEAN partially generalises to emerging fake
news as it learns from relevant external knowledge. We compare Cross-SEAN with
seven state-of-the-art fake news detection methods. We observe that it achieves
$0.95$ F1 Score on CTF, outperforming the best baseline by $9\%$. We also
develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time
detection of fake tweets.
