  The abundance of information in digital media, which in today's world is the
main source of knowledge about current events for the masses, makes it possible
to spread disinformation on a larger scale than ever before. Consequently,
there is a need to develop novel fake news detection approaches capable of
adapting to changing factual contexts and generalizing previously or
concurrently acquired knowledge. To deal with this problem, we propose a
lifelong learning-inspired approach, which allows for fake news detection in
multiple languages and the mutual transfer of knowledge acquired in each of
them. Both classical feature extractors, such as Term frequency-inverse
document frequency or Latent Dirichlet Allocation, and integrated deep NLP
(Natural Language Processing) BERT (Bidirectional Encoder Representations from
Transformers) models paired with MLP (Multilayer Perceptron) classifier, were
employed. The results of experiments conducted on two datasets dedicated to the
fake news classification task (in English and Spanish, respectively), supported
by statistical analysis, confirmed that utilization of additional languages
could improve performance for traditional methods. Also, in some cases
supplementing the deep learning method with classical ones can positively
impact obtained results. The ability of models to generalize the knowledge
acquired between the analyzed languages was also observed.
