A deep neural network approach for fake news detection using linguistic and psychological features

Abstract

With the prominence of online social networks, news has become more accessible to a global audience. However, in the meantime, it has become increasingly difficult for individuals to differentiate between real and fake news. To reduce the spread of fake news, researchers have developed different classification models to identify fake news. In this paper, we propose a fake news detection system using a multilayer perceptron (MLP) model, which leverages linguistic and psychological features to determine the truthfulness of a news article. The model uses different features from the article’s text content to detect fake news. In the experiment, we utilize a public dataset from the FakeNewsNet repository consisting of real and fake news articles collected from PolitiFact and BuzzFeed. We perform a meta-analysis to compare our model’s performance with existing classification models using the same feature sets and evaluate the performance using the metrics such as prediction accuracy and F1 score. Overall, our classification model produces better results than existing baseline models, by achieving an accuracy and F1 score above 90 % and performs 3% better than the best performing baseline method. The inclusion of linguistic and psychological features with a deep neural network allows our model to consistently and accurately classify fake news with ever-changing forms of news events.