Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
This addresses the problem of fake news spread on social media for users and platforms, but it is incremental as it builds on existing datasets and models.
The paper tackles automated detection of false short-text claims on social media by extending the LIAR dataset with sentiment and emotion features and proposing a BERT-based deep learning architecture, achieving 70% accuracy, a ~30% improvement over prior results.
The rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history. However, the inherently unsupervised nature of social media platforms has also made it easier to spread false information and fake news. Furthermore, the high volume and velocity of information flow in such platforms make manual supervision and control of information propagation infeasible. This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media. We first introduce Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims. Furthermore, we propose a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake. Our results demonstrate that the proposed architecture trained on Sentimental LIAR can achieve an accuracy of 70%, which is an improvement of ~30% over previously reported results for the LIAR benchmark.