  Fake News Detection is an essential problem in the field of Natural Language
Processing. The benefits of an effective solution in this area are manifold for
the goodwill of society. On a surface level, it broadly matches with the
general problem of text classification. Researchers have proposed various
approaches to tackle fake news using simple as well as some complex techniques.
In this paper, we try to make a comparison between the present Deep Learning
techniques by representing the news instances in some vector space using a
combination of common mathematical operations with available vector space
representations. We do a number of experiments using various combinations and
permutations. Finally, we conclude with a sound analysis of the results and
evaluate the reasons for such results.
