  Fake news is a growing challenge for social networks and media. Detection of
fake news always has been a problem for many years, but after the evolution of
social networks and increasing speed of news dissemination in recent years has
been considered again. There are several approaches to solving this problem,
one of which is to detect fake news based on its text style using deep neural
networks. In recent years, one of the most used forms of deep neural networks
for natural language processing is transfer learning with transformers. BERT is
one of the most promising transformers who outperforms other models in many NLP
benchmarks. This article, we introduce MWPBert, which uses two parallel BERT
networks to perform veracity detection on full-text news articles. One of the
BERT networks encodes news headline, and another encodes news body. Since the
input length of the BERT network is limited and constant and the news body is
usually a long text, we cannot fed the whole news text into the BERT.
Therefore, using the MaxWorth algorithm, we selected the part of the news text
that is more valuable for fact-checking, and fed it into the BERT network.
Finally, we encode the output of the two BERT networks to an output network to
classify the news. The experiment results showed that the proposed model
outperformed previous models in terms of accuracy and other performance
measures.
