Social media platforms have seen a major boom in the past decade. Apart
from entertainment and establishing social connections, the use of
social media to read news articles has become prevalent. News has not
only become less costly but also fast and accessible. However, accessing
news via social media has its own demerits. It is next to impossible for
the end users to establish the validity of the news articles. Manual
review of each and every article on the internet is neither quick nor
economically feasible, considering the vast amount of news articles that
are published on day-to-day basis. Hence, there arises a need for quick
and highly accurate machine learning models to detect the fake news
articles. In this paper, we propose a method to detect fake news via
Graph based Markov chains. We initiate the classification process by
first segregating the fake news articles from the real ones, then
training two separate Markov Chains for both the classes, and finally
calculating the probability that a news article was generated by a given
Markov Chain. Our approach of establishing similarity between a text
sequence and a Markov chain relies on probabilistic and statistical
calculations, attained by performing random walks on the Markov chains.
Such an approach has proven to provide a high accuracy on multiple
datasets.