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As the COVID-19 pandemic sweeps across the world, it has been accompanied bya tsunami of fake news and misinformation on social media. At the time whenreliable information is vital for public health and safety, COVID-19 relatedfake news has been spreading even faster than the facts. During times such asthe COVID-19 pandemic, fake news can not only cause intellectual confusion butcan also place lives of people at risk. This calls for an immediate need tocontain the spread of such misinformation on social media. We introduce CTF,the first COVID-19 Twitter fake news dataset with labeled genuine and faketweets. Additionally, we propose Cross-SEAN, a cross-stitch basedsemi-supervised end-to-end neural attention model, which leverages the largeamount of unlabelled data. Cross-SEAN partially generalises to emerging fakenews as it learns from relevant external knowledge. We compare Cross-SEAN withseven 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 alsodevelop Chrome-SEAN, a Cross-SEAN based chrome extension for real-timedetection of fake tweets.

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection