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

TL;DR

This paper introduces Sentimental LIAR, an extended dataset with sentiment features, and a BERT-based deep learning model that significantly improves fake claim detection accuracy on social media.

Contribution

It presents a new dataset extension with sentiment analysis and a BERT-based model for more accurate fake claim classification.

Findings

Achieved 70% accuracy on fake claim detection

Extended LIAR dataset with sentiment and emotion features

Improved accuracy by approximately 30% over previous methods

Abstract

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.