A Multi-Policy Framework for Deep Learning-Based Fake News Detection

TL;DR

This paper presents a multi-policy deep learning framework called MPSC for detecting fake news by analyzing statements and related articles, demonstrating reliable identification of suspicious content.

Contribution

The paper introduces MPSC, a novel multi-policy framework that combines multiple deep learning models to improve fake news detection accuracy.

Findings

Multi-policy analysis effectively identifies suspicious statements.

BERT-based models outperform other architectures in detection accuracy.

Framework tested on merged datasets with promising results.

Abstract

Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information. However, a more interconnected society enables the spread of intentionally false information. To mitigate the negative impacts of fake news, it is essential to improve detection methodologies. This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection by using deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious. The proposed framework was evaluated using four merged datasets containing real and fake news. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) models were trained to utilize both lexical and syntactic features, and their performance was evaluated. The obtained results demonstrate that a multi-policy analysis reliably identifies suspicious statements, which can be advantageous for fake news detection.