  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.
