A Multi-Policy Framework for Deep Learning-Based Fake News Detection
This work addresses the problem of fake news spread for society by proposing an incremental improvement in detection methodologies.
The paper tackles fake news detection by introducing the Multi-Policy Statement Checker (MPSC) framework, which uses deep learning models like LSTM, GRU, and BERT to analyze statements and related articles, achieving reliable identification of suspicious statements as evaluated on four merged datasets.
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.