Efficient Fake News Detection using Bagging Ensembles of Bidirectional Echo State Networks

The dissemination of fake news is one of the most concerning issues in current digital media platforms, originating from the quick and easy spread of unverified information therethrough. Consequently, intense research efforts have been invested towards automating the process of identifying fake news from textual data by means of Artificial Intelligence methods. Among the manifold approaches proposed for this purpose to date, a large fraction of studies have examined the performance of modern deep neural network architectures, mostly relying on pretrained word embeddings and neural processing modules of diverse kind. Unfortunately, such sophisticated Deep Learning methods often require intense computational efforts for training. In this work we explore a novel approach based on randomization-based recurrent neural networks. Specifically, our proposal consists of a weighted ensemble of bidirectional Echo State Networks learned from word sequences processed through pretrained embeddings. Experiments over two fake news detection datasets reveal that competitive detection statistics are obtained by our proposed approach when compared to shallow learning and avant-garde Deep Learning models, but at a dramatically less computational complexity in their training phase.