A Machine Learning Approach for Fake News Detection

Abstract

A vital role is played by the media in the circulation of public information about the happenings. A fast spread of information using social media and social networks or websites is possible due to the quick development of the internet. With no worry about the reliability of the information, the misinformation is spread via social websites or networks and reaches multiple users. It is a very big challenge for society to deal with the spread of misinformation. The present research interest is the analysis of instinctive credibility of the news articles. For linguistic modeling, LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), etc. are widely utilized deep learning models. This paper presents a Bidirectional LSTM (BiLSTM) based sequential model for the detection of fake news. The proposed model uses word embeddings for the representation of words in high dimensional vector format. These word embeddings and Bidirectional LSTM tries to incorporate more semantic information of the content than that of RNN and unidirectional LSTM. 3 different publicly available datasets from Kaggle dataset repository are combined together for training, testing and validation of the proposed model. Along with this, the performance of different word embeddings is analyzed in this paper.