Fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning

K. M. Nikitha, Ryan Rozario, Chinmayan Pradeep, V. S. Ananthanarayana

Abstract: Since its conception roughly 40 years ago, the Internet has always been an unpoliced area of human interaction. This lawlessness has since been curbed with legislation, making nefarious activities on the web constitutionally punishable. However, in the case of fake news and disinformation campaigns, the responsibility of verification is placed on the reader and the publisher, and there is no easily executable legal recourse for wrongdoers. This lack of policing combined with the power of controlling popular opinion for uses such as election manipulation, slander as a form of blackmail, stock manipulation for insider trading, shielding corporate wrong-doing makes it clear that this is a problem worth solving. Furthermore, we believe that automating the process is crucial as the task requires processing a massive amount of information whilst also being free of all biases, which is not possible by a human team. This paper explores different text properties that can indicate if a newspaper article is likely to be false or real. Our novel approach makes use of an ensemble learner created using weak learners. The weak learners are further trained on selective features to make them moderate learners. Our study shows that training individual models on different sets of features extracted using genetic algorithms performs better than models trained on all features. These become moderate learners and surpass the weak learners on performance. Further, when we ensemble these moderate learners, we achieve superior results than normal ensemble learners.

External IDs:doi:10.1007/978-981-19-0840-8_27