Fake News Stance Detection using Deep Neural Network

Abstract

With the advancement of technology, fake news is more widely exposed to users. Fake news may be found on the Internet, news sources and social media platforms. The spread of the fake news has harmed both individuals and society. The way to observe fake news using the stance detection technique is the focus of this paper. Given a set of news body and headline pairs, stance detection is the task of automatic detection of relationships among pieces of text. Pre-trained GloVe word embedding is used for the word to vector representation as it can capture the inter-word semantic information. The LSTM neural network had been shown efficient in deep learning applications because it can capture sequential information of input data. In this paper, it is found that the LSTM-based encoding decoding model using pre-trained GloVe word embedding achieved 93.69% accuracy on the FNC-1 dataset.