A Novel Fake-News Dataset and Detection System to Mitigate Cyber War with Emphasis on Nigerian News Events

Abstract

Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can result to the breakdown of law and order in the society both physically and on the cyber-social-space. In Nigeria, there is currently no established law that guides the use of social media. Therefore, the rate at which fake-news propagates is alarming. This paper presents a new dataset, with focus on Nigeria’s trending news such as EndSARS and Herdsmen attacks, which was further used to simulate Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) machine learning models to detect fake-news. The data were extracted from twitter using twitter Application Package Interface (API) and from facebook using a scraping tool. The dataset was encoded using Unicode escape function in python to make all characters accessible by the algorithm and tokenised using Global Vectors for Word Representation. The dataset was used to train CNN and RNN models built in python on google colab platform to detect fake-news using accuracy, sensitivity, recall and F1 score as evaluation metrics. Results showed that RNN performed better in terms of accuracy and precision, at 82.34% and 93.19% compared to 81.96% and 79.65% for CNN, F1 scores are approximately the same for both models and CNN performed better than RNN in terms of recall at 98.03% to 50.61% for RNN.