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The rampant integration of social media in our every day lives and culturehas given rise to fast and easier access to the flow of information than everin human history. However, the inherently unsupervised nature of social mediaplatforms has also made it easier to spread false information and fake news.Furthermore, the high volume and velocity of information flow in such platformsmake manual supervision and control of information propagation infeasible. Thispaper aims to address this issue by proposing a novel deep learning approachfor automated detection of false short-text claims on social media. We firstintroduce Sentimental LIAR, which extends the LIAR dataset of short claims byadding features based on sentiment and emotion analysis of claims. Furthermore,we propose a novel deep learning architecture based on the BERT-Base languagemodel for classification of claims as genuine or fake. Our results demonstratethat the proposed architecture trained on Sentimental LIAR can achieve anaccuracy of 70%, which is an improvement of ~30% over previously reportedresults for the LIAR benchmark.

Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification