Deep learning-based fake news detection

Abstract

Identifying the truthfulness of news is crucial as it has a great societal impact, and its importance has increased every year since the information age. After the deep learning models were introduced to generate fake news, it become more difficult for a human to identify fake news. Therefore, researchers proposed neural network models to detect fake news but most models only focus on a few datasets. This dissertation evaluates different types of methods on various datasets for overall performance. Furthermore, we discuss the application range of different types of detection methods.