Fake News Detection Utilizing Textual Cues

Abstract

Easy and quick information diffusion on the web and especially in social media has been rapidly proliferating during the past decades. As information is posted without any kind of verification of its veracity, fake news has become a problem of great influence in our information driven society. Thus, to mitigate the consequences of fake news and its propagation, automated approaches to detect malicious content were created. This paper proposes an effective framework that utilizes only the text features of the news. We evaluate several features for differentiating fake from real news and we identify the best performing feature set that maximizes performance, using feature selection techniques. Text representation features were also explored as a potential solution. Additionally, the most popular Machine Learning and Deep Learning models were tested to conclude to the model that achieves the maximum accuracy. Our findings reveal that a combination of linguistic features and text-based word vector representations through ensemble methods can predict fake news with high accuracy. eXtreme Gradient Boosting (XGB) outperformed all other models, while linear Support Vector Machine (SVM) achieved comparable results.