  Tabular data is considered the last unconquered castle of deep learning, yet
the task of data stream classification is stated to be an equally important and
demanding research area. Due to the temporal constraints, it is assumed that
deep learning methods are not the optimal solution for application in this
field. However, excluding the entire -- and prevalent -- group of methods seems
rather rash given the progress that has been made in recent years in its
development. For this reason, the following paper is the first to present an
approach to natural language data stream classification using the sentence
space method, which allows for encoding text into the form of a discrete
digital signal. This allows the use of convolutional deep networks dedicated to
image classification to solve the task of recognizing fake news based on text
data. Based on the real-life Fakeddit dataset, the proposed approach was
compared with state-of-the-art algorithms for data stream classification based
on generalization ability and time complexity.
