Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media

Abstract

The prevalence of fake news on social media has led to major sociopolitical issues. Thus, the need for automated fake news detection is more important than ever. In this work, we investigated the interplay between news content and users’ posting behavior clues in detecting fake news by using state-of-the-art deep learning approaches, such as the convolutional neural network (CNN), which involves a series of filters of different sizes and shapes (combining the original sentence matrix to create further low-dimensional matrices), and the bidirectional gated recurrent unit (BiGRU), which is a type of bidirectional recurrent neural network with only the input and forget gates, coupled with a self-attention mechanism. The proposed architectures introduced a novel approach to learning rich, semantical, and contextual representations of a given news text using natural language understanding of transfer learning coupled with context-based features. Experiments were conducted on the FakeNewsNet dataset. The experimental results show that incorporating information about users’ posting behaviors (when available) improves the performance compared to models that rely solely on textual news data.