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Social media platforms like Facebook, Twitter, and Instagram have enabledconnection and communication on a large scale. It has revolutionized the rateat which information is shared and enhanced its reach. However, another side ofthe coin dictates an alarming story. These platforms have led to an increase inthe creation and spread of fake news. The fake news has not only influencedpeople in the wrong direction but also claimed human lives. During thesecritical times of the Covid19 pandemic, it is easy to mislead people and makethem believe in fatal information. Therefore it is important to curb fake newsat source and prevent it from spreading to a larger audience. We look atautomated techniques for fake news detection from a data mining perspective. Weevaluate different supervised text classification algorithms on Contraint@AAAI2021 Covid-19 Fake news detection dataset. The classification algorithms arebased on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM),and Bidirectional Encoder Representations from Transformers (BERT). We alsoevaluate the importance of unsupervised learning in the form of language modelpre-training and distributed word representations using unlabelled covid tweetscorpus. We report the best accuracy of 98.41\% on the Covid-19 Fake newsdetection dataset.

Evaluating Deep Learning Approaches for Covid19 Fake News Detection